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<reponame>Hiwen-STEM/MaxHeap #Author: <NAME> #Non-Profit Company: Inventorsniche L.L.C #Project: Memmap Based Binary Tree #Date Created: May 10th, 2021 #Purpose: The purpose of this project is to create an efficient binary heap # that re-directs the majority of what would be memory related consumption towards # storage; this is done by creating a file in storage that will contain all the data. # This way a reference to the storage file can be made and used to access small parts of # the file in storage. The size of the storage files are kept at 1000x1000 matrice segments. # Once a matrix is filled up, then another 1000x1000 matrix file will be created in storage. # By doing this we can also keep the amount of storage used at a minimal amount, but this does # assume that the file associated with a 1000x1000 matrix is not that big. For many it isn't # considering the amount of storage most computers come with these days, but for some it could be # a problem; so keep this into consideration before using... # #Functionality: #Import all required modules for the efficient Binary Tree... ############################################################# ############################################################# # #The numpy module contains the memmap that will be used... import numpy as np # #The OS package will be used to make new directories and #detect already existing files... import os # #The glob package will be used to aid in naming directories #correctly based on pre-existing ones... import glob # #Use the re package in order to order files during #re-calibration... import re # ############################################################# #This section is reserved for all core functions and helper functions... ######################################################################## ######################################################################## #The first implementation of the createBT (BT = Binary Tree) function, #by default, creates a 1000x1 (1000 rows, 1 column) memmap matrix. #This matrix will be capable of holding a max of 1000 binary tree #elements. ***NOTE: Upon adding more than 1000 elements, the matrix will #resize automatically. def createBTO(): #binary tree array... BT = [] #binary tree info array... INFO = [] #make the new directory for all operations... Data = makeDir() #create a 1000x1 memmap matrix for the efficient binary tree #implementation... tree = np.memmap("0", dtype='float32', mode='w+', shape=(1000,1000)) #append tree to the global BT variable... BT.append(tree) #append BT to the info array... INFO.append(BT) #append 0, for LEVEL 0... INFO.append(0) #append 0, for INDEX 0... INFO.append(-1) #append 0, for 0 nodes... INFO.append(0) #append Data file reference... INFO.append(Data) #change data value... INFO[4][0][0] = -1 #return INFO... return INFO #Breadth first search algorithm... def BreadthFS(BT, INDEX, value): #go through all memmap cells in a linear format... for x in range(INDEX + 1): #Decompose the index value... a, b, c = Decomp(x) #check for equivalence... if(float(str(BT[int(a)][int(b)][int(c)])) == float(value)): #return the coordinates... return int(a), int(b), int(c) #return -1 since no match was found return -1, -1, -1 #Get the height of a particular node index... def getHeightOne(index, INDEX, LEVEL): #First check to see if the index is valid... if(not(index >= 0 and index <= INDEX)): #return value of -1 since the index is not #valid... return -1 #check to see if index == 0 if(index == 0): #return level zero... return 0 #sum variable... sum = 0 #See what level the index is on... for x in range(1, LEVEL + 1): #sum the number of nodes on each level... sum = sum + 2**x #check if the index is on the current level... if(index <= sum): #return the level number... return x #Get the height of a particular node value... def getHeightTwo(value, INDEX, LEVEL, BT): #Use Breadth first search to find the value... a, b, c = BreadthFS(BT, INDEX, value) #get the final height of the value, but first make sure #the value is valid... if(a != -1): #compute the index based on the three components a,b,c... index = ((c*1000) + b) + (a*1000000) #get the final height of the value... height = getHeightOne(index, INDEX, LEVEL) #return the retrieved height... return height #return -1 if the value could not be found... return None #Resize the memmap if more room needs to be made... def MResize(BT): #since cols == 1000, we need a whole new memmap... tree = np.memmap(str(len(BT)), dtype='float32', mode='w+', shape=(1000,1000)) #Now append the new memmap to the BT list... BT.append(tree) #Check if the binary tree is a perfect full tree def isPerfectFullTree(NUMNODES, LEVEL): #sum variable... sum = 0 #Check if NUMNODES is equal to the summation of 2^n from n=0 to n = level... for x in range(LEVEL + 1): #add 2^x to sum... sum = sum + (2**x) #check if sum is equal to NUMNODES... if(NUMNODES == sum): #return 1 since the tree is full return 1 else: #return -1 since the tree is not full return -1 #check if the binary tree is a full tree... def isFullTree(INFO): #see if the number of nodes is odd or not... if(INFO[3] % 2 == 0 or INFO[3] == 0): #return error code... return -1 else: return 1 #Decompose Index... def Decomp(index): #convert index to string... index = str(index) #get the length of string index... length = len(index) #segment length variable... seg = None #row, column, and memmap # variables... row = None col = None memNum = None #flag variable representing a length less than 3... flag = 0 #get row number... if(length >= 3): #assign 3 seg = 3 elif(length < 3): #assign length to seg... seg = length #set flag to 1... flag = 1 #now extract the row... row = index[(length - seg):] #make sure a column can be extracted... if(flag != 1): #get column number... if(length >= 6): #assign 6 seg = 3 elif(length > 3): #assign length - 3 seg = length - 3 #assign column number... col = index[(length - 3) - seg:length - 3] #check if col is empty string.. if(col == ""): #set col to zero col = 0 else: #assign zero value to col... col = 0 #now get the memmap number... if(length > 6): #assign value to memNum... memNum = index[0:length - 6] else: #assign value zero to memNum... memNum = 0 #now return all three values... return memNum, row, col #return the max value of the max-heap... def getMax(BT): #return the root node value... return BT[0][0][0] #Traverse left... def LeftChild(index): #compute the child index... left = (index*2) + 1 #return the computed index... return left #Traverse right... def RightChild(index): #compute the child index... right = (index*2) + 2 #return the computed index... return right #extract the max value... def ExtractMax(BT, INDEX, LEVEL): #remove value from supernode (root) value = BT[0][0][0] #move downwards until an appropriate place #for the root replacement is found. #first decompose the last index location... a, b, c = Decomp(INDEX) #Replace root with last index value... BT[0][0][0] = BT[int(a)][int(b)][int(c)] #decrement index... INDEX = INDEX - 1 #update the LEVEL variable... if((INDEX == 0 or INDEX == -1) and LEVEL != 0): LEVEL = 0 else: #see if LEVEL needs to be incremented... sum = 0 #sum all the way to LEVEL... for x in range(1, LEVEL+1): #increment sum... sum = sum + 2**x #increment LEVEL based on sum... if(INDEX <= sum): #increment LEVEL... LEVEL = x #break out of for loop... break #move the new root value to appropriate place... return downwardT(value, INDEX, LEVEL, BT) #add value to the MAX-Binary-Heap... def MAXBTAdd(BT, LEVEL, INDEX, NUMNODES, value): #decompose the next index... a, b, c = Decomp(INDEX + 1) #see if resizing is necessary... if(INDEX+1 > ((len(BT)-1)*1000000 + (BT[len(BT)-1].shape[1]-1)*1000 + 999)): #resize the memmap... MResize(BT) #first add the value to the position #INDEX + 1 #starting index for upward traversal... index = INDEX + 1 #see if LEVEL needs to be incremented... sum = 0 #sum all the way to LEVEL... for x in range(1, LEVEL+1): #increment sum... sum = sum + 2**x #increment LEVEL based on sum... if(INDEX+1 > sum): #increment LEVEL... LEVEL = LEVEL + 1 #assign value to position BT[a][b][c] BT[int(a)][int(b)][int(c)] = float(value) #traverse new value upwards to appropriate place... upwardT(a, b, c, index, value, BT) #return the new INDEX and LEVEL... return LEVEL, (INDEX + 1), (NUMNODES + 1) #Traverse Back left... def LeftBack(index): #compute the parent index... parent = (index - 1) / 2 #return parent return parent #Traverse Back right... def RightBack(index): #compute the parent index... parent = (index - 2) / 2 #return the parent index return parent #split the input and create binary heaps #based on the split input... def splitInput(List, numElements): #index multiplier.. index = len(numElements) - 3 #length multiplier... length = len(numElements) #temporary memmap reference... tree = None #memmap column count... col = 0 #memmap structure count... structure = 0 #negative number flag... negative = 0 #use while loop to get number of columns... for x in range(2): #see if this is the first iteration... if(x == 0): #see if the last three digits are all zeros... temp = numElements[index:length] #check for all zeros... if(int(temp) != 0): #get the first segment... col = col + 1 else: #see if index is zero after subtracting another three... if(index - 3 > 0): #update col... col = col + int(numElements[index-3:length-3]) else: #update col... col = col + int(numElements[0:length-3]) #set negative flag... negative = 10 #get number of structures... if(negative != 10): #get structure count... structure = int(numElements[0:length-6]) #Now create structures... createStructure(List, structure, col) #create the number of strucutres calculated in #split input function... def createStructure(List, structure, columns): #number tracker... num = 0 #create the structures... for x in range(structure): #get reference... tree = np.memmap(str(x), dtype='float32', mode='w+', shape=(1000,1000)) #append to the list... List.append(tree) #see if x is equal to structure - 1... if(x == structure - 1): #assign x+1 to num... num = str(x+1) #see if another strucutre is needed... if(columns != 0): #create last structure... tree = np.memmap(str(num), dtype='float32', mode='w+', shape=(1000,columns)) #append to the list... List.append(tree) #downwards traversal function... def downwardT(value, INDEX, LEVEL, BT): #index value... index = 0 #index components... x, y, z = 0, 0, 0 a, b, c = 0, 0, 0 #use while loop to perform downward traversal... while(True): #start traversing downwards... temp1 = LeftChild(index) temp2 = RightChild(index) #start downward traversal... if(temp1 <= INDEX and temp2 <= INDEX): #decompose index values... a, b, c = Decomp(temp1) t, q, p = Decomp(temp2) #see which value is larger... if(not(BT[int(t)][int(q)][int(p)] < BT[int(a)][int(b)][int(c)])): #reassign values... a, b, c = t, q, p #assign index value index = temp2 else: #assign index value... index = temp1 elif(temp1 <= INDEX): #decompose index value... a, b, c = Decomp(temp1) #adjust temp value index = temp1 elif(temp2 <= INDEX): #decompose index value... a, b, c = Decomp(temp2) #adjust index value.. index = temp2 #see if new root value is in the right spot... if(BT[int(x)][int(y)][int(z)] < BT[int(a)][int(b)][int(c)]): #switch the values... val = BT[int(x)][int(y)][int(z)] #assign the larger value... BT[int(x)][int(y)][int(z)] = BT[int(a)][int(b)][int(c)] #assign val to BT[a][b][c] BT[int(a)][int(b)][int(c)] = val #copy the index components a,b,c x, y, z = a, b, c #continue early on... continue #return the INDEX value and MAX value... return value, INDEX, LEVEL, (INDEX + 1) #upward traveral function... def upwardT(a, b, c, index, value, BT): #x, y, z checkpoint values.... x, y, z = 0, 0 , 0 #use while loop to traverse upwards... while(True): #first try backtracking from the left... temp = LeftBack(index) #see if it is a decimal or not... if(temp < 0 or int(str(temp)[str(temp).index('.')+1:]) != 0): #change temp... temp = RightBack(index) #make sure temp is not negative... #The following is a flag variable... flag = None #The comparison to see if temp is less than #zero... if(temp < 0): #set flag to 1... flag = 1 else: #decompose temp... x, y, z = Decomp(int(temp)) #make comparison to see if upwards #traversal is necessary... if((flag != 1) and (BT[int(x)][int(y)][int(z)] < value)): #exchange values... history = BT[int(x)][int(y)][int(z)] #assign value to x, y, z location... BT[int(x)][int(y)][int(z)] = value #assign history to a, b, c location... BT[int(a)][int(b)][int(c)] = history #update index components... a, b, c = x, y, z #update index... index = temp #continue.. continue #break from the loop if this point is reached... break #User function for adding a value... def AddValue(INFO,value): #see if the INFO Structure appears legit... if(Secure(INFO) == 1 and (type(value) == type(9.4) or type(value) == type(2))): #retrieve level, index, and num-nodes... INFO[1], INFO[2], INFO[3] = MAXBTAdd(INFO[0],INFO[1],INFO[2],INFO[3],value) #add the new index to the Data file... INFO[4][0][0] = INFO[2] #return value return value else: #return None return None #User function for extracting the max value... def ExtractMaxValue(INFO): #see if the INFO structure appears legit... if(Secure(INFO) == 1 and INFO[2] != -1 and INFO[3] > 0): #UPDATE the INFO list and retrieve the extracted value... value, INFO[2], INFO[1], INFO[3] = ExtractMax(INFO[0], INFO[2], INFO[1]) #update Data index... INFO[4][0][0] = INFO[2] #return the extracted value... return value else: return None #User function for retrieving the max value without deletion... def getMaxValue(INFO): #see if the INFO structure appears legit... if(Secure(INFO) == 1 and INFO[2] != -1 and INFO[3] > 0): #return the max value... return getMax(INFO[0]) else: return None #User friendly breadth first search function... def BreadthFirstOne(INFO, value): #see if the INFO structure appears legit... if(Secure(INFO) == 1 and INFO[2] != -1 and INFO[3] > 0): #return success or failure... return BreadthFS(INFO[0], INFO[2], value) else: return -1,-1,-1 #security function... def Secure(INFO): #check if index is valid... if(INFO[2] < -1): #return -1 error code... return -1 elif(INFO[1] < 0): #return -1 error code... return -1 elif(INFO[3] >= 1 and (getHeightOne(INFO[2], INFO[2], INFO[1]) != INFO[1])): #return -1 error code... return -1 elif(INFO[3] >= 1 and (INFO[3] - 1 != INFO[2])): #return -1 error code... return -1 else: #return success code... return 1 #User is full tree function... def isPerfect(INFO): #return full tree code or not full #tree code... return isPerfectFullTree(INFO[3], INFO[1]) #re-calibrate the information list... def reCalibrateInfo(): #make sure that the globbed files are from a .BinaryT directory... directory = os.getcwd() #now check for the .BinaryT extension... if(not(".BinaryT" in directory)): #return -1 error code... return -1 else: #files list... L = [] #information list... INFO = [] #now glob all the files... for files in glob.glob("*"): #append files to L... L.append(files) #remove Data file... if("Data.Data" in L): L.remove("Data.Data") else: return -1 #check if L is full... if(len(L) == 0): #return error code -1... return -1 else: #new file list... L2 = [] #sort the list... for x in range(len(L)): #use regular expression to see if the file #should be added in a certain position... for y in range(len(L)): #sub the x segment of the file name with nothing... r = re.sub(str(x), "", L[y]) #add file reference if a match is made... if(r == ""): #append memmap reference to L2... L2.append(np.memmap(L[y], dtype='float32', mode='r+', shape=(1000,1000))) #break out of loop... break #append files list to info list... INFO.append(L2) #now append INDEX, LEVEL, AND NUMNODES... ref = np.memmap("Data.Data", dtype='float32', mode='r+', shape=(1,1)) #append LEVEL... if(ref[0][0] == -1): INFO.append(0) else: INFO.append(getHeightOne(int(ref[0][0]),int(ref[0][0]),10000)) #append INDEX... INFO.append(int(ref[0][0])) #append numnodes... INFO.append(int(ref[0][0]) + 1) #append data file reference... INFO.append(ref) #one final check... a, b, c = Decomp(int(INFO[4][0][0])) if(len(INFO[0])-1 != int(a)): #return error code... return -1 #return the INFO list... return INFO #create new directory and change to it... def makeDir(): #files... L = [] #glob files together... for files in glob.glob("*.BinaryT"): #append the files... L.append(files) #make the new directory... os.mkdir("Max_Heap_Tree_Files"+str(len(L)+1)+".BinaryT") #change operations to the new directory... os.chdir("Max_Heap_Tree_Files"+str(len(L)+1)+".BinaryT") #return a reference to the new Data file... return np.memmap("Data.Data", dtype='float32', mode='w+', shape=(1,1)) #user function for getting the height of a certain value... def getHeightThree(INFO, value): #return the height... return getHeightTwo(value, INFO[2], INFO[1], INFO[0])
<filename>pooch/tests/test_processors.py<gh_stars>1-10 """ Test the processor hooks """ from pathlib import Path from tempfile import TemporaryDirectory import warnings import pytest from .. import Pooch from ..processors import Unzip, Untar, ExtractorProcessor, Decompress from .utils import pooch_test_url, pooch_test_registry, check_tiny_data, capture_log REGISTRY = pooch_test_registry() BASEURL = pooch_test_url() @pytest.mark.parametrize( "method,ext,name", [ ("auto", "xz", None), ("lzma", "xz", None), ("xz", "xz", None), ("bzip2", "bz2", None), ("gzip", "gz", None), ("gzip", "gz", "different-name.txt"), ], ids=["auto", "lzma", "xz", "bz2", "gz", "name"], ) def test_decompress(method, ext, name): "Check that decompression after download works for all formats" processor = Decompress(method=method, name=name) with TemporaryDirectory() as local_store: path = Path(local_store) if name is None: true_path = str(path / ".".join(["tiny-data.txt", ext, "decomp"])) else: true_path = str(path / name) # Setup a pooch in a temp dir pup = Pooch(path=path, base_url=BASEURL, registry=REGISTRY) # Check the logs when downloading and from the processor with capture_log() as log_file: fname = pup.fetch("tiny-data.txt." + ext, processor=processor) logs = log_file.getvalue() lines = logs.splitlines() assert len(lines) == 2 assert lines[0].split()[0] == "Downloading" assert lines[-1].startswith("Decompressing") assert method in lines[-1] assert fname == true_path check_tiny_data(fname) # Check that processor doesn't execute when not downloading with capture_log() as log_file: fname = pup.fetch("tiny-data.txt." + ext, processor=processor) assert log_file.getvalue() == "" assert fname == true_path check_tiny_data(fname) def test_decompress_fails(): "Should fail if method='auto' and no extension is given in the file name" with TemporaryDirectory() as local_store: path = Path(local_store) pup = Pooch(path=path, base_url=BASEURL, registry=REGISTRY) # Invalid extension with pytest.raises(ValueError) as exception: with warnings.catch_warnings(): pup.fetch("tiny-data.txt", processor=Decompress(method="auto")) assert exception.value.args[0].startswith("Unrecognized file extension '.txt'") assert "pooch.Unzip/Untar" not in exception.value.args[0] # Should also fail for a bad method name with pytest.raises(ValueError) as exception: with warnings.catch_warnings(): pup.fetch("tiny-data.txt", processor=Decompress(method="bla")) assert exception.value.args[0].startswith("Invalid compression method 'bla'") assert "pooch.Unzip/Untar" not in exception.value.args[0] # Point people to Untar and Unzip with pytest.raises(ValueError) as exception: with warnings.catch_warnings(): pup.fetch("tiny-data.txt", processor=Decompress(method="zip")) assert exception.value.args[0].startswith("Invalid compression method 'zip'") assert "pooch.Unzip/Untar" in exception.value.args[0] with pytest.raises(ValueError) as exception: with warnings.catch_warnings(): pup.fetch("store.zip", processor=Decompress(method="auto")) assert exception.value.args[0].startswith("Unrecognized file extension '.zip'") assert "pooch.Unzip/Untar" in exception.value.args[0] def test_extractprocessor_fails(): "The base class should be used and should fail when passed to fecth" with TemporaryDirectory() as local_store: # Setup a pooch in a temp dir pup = Pooch(path=Path(local_store), base_url=BASEURL, registry=REGISTRY) processor = ExtractorProcessor() with pytest.raises(NotImplementedError) as exception: pup.fetch("tiny-data.tar.gz", processor=processor) assert "'suffix'" in exception.value.args[0] processor.suffix = "tar.gz" with pytest.raises(NotImplementedError) as exception: pup.fetch("tiny-data.tar.gz", processor=processor) assert not exception.value.args @pytest.mark.parametrize( "proc_cls,ext", [(Unzip, ".zip"), (Untar, ".tar.gz")], ids=["Unzip", "Untar"] ) def test_processors(proc_cls, ext): "Setup a hook and make sure it's only executed when downloading" processor = proc_cls(members=["tiny-data.txt"]) suffix = proc_cls.suffix extract_dir = "tiny-data" + ext + suffix with TemporaryDirectory() as local_store: path = Path(local_store) true_path = str(path / extract_dir / "tiny-data.txt") # Setup a pooch in a temp dir pup = Pooch(path=path, base_url=BASEURL, registry=REGISTRY) # Check the logs when downloading and from the processor with capture_log() as log_file: fnames = pup.fetch("tiny-data" + ext, processor=processor) fname = fnames[0] assert len(fnames) == 1 logs = log_file.getvalue() lines = logs.splitlines() assert len(lines) == 2 assert lines[0].split()[0] == "Downloading" assert lines[-1].startswith("Extracting 'tiny-data.txt'") assert fname == true_path check_tiny_data(fname) # Check that processor doesn't execute when not downloading with capture_log() as log_file: fnames = pup.fetch("tiny-data" + ext, processor=processor) fname = fnames[0] assert len(fnames) == 1 assert log_file.getvalue() == "" assert fname == true_path check_tiny_data(fname) @pytest.mark.parametrize( "proc_cls,ext,msg", [(Unzip, ".zip", "Unzipping"), (Untar, ".tar.gz", "Untarring")], ids=["Unzip", "Untar"], ) def test_processor_multiplefiles(proc_cls, ext, msg): "Setup a processor to unzip/untar a file and return multiple fnames" processor = proc_cls() suffix = proc_cls.suffix extract_dir = "store" + ext + suffix with TemporaryDirectory() as local_store: path = Path(local_store) true_paths = { str(path / extract_dir / "store" / "tiny-data.txt"), str(path / extract_dir / "store" / "subdir" / "tiny-data.txt"), } # Setup a pooch in a temp dir pup = Pooch(path=path, base_url=BASEURL, registry=REGISTRY) # Check the logs when downloading and from the processor with capture_log() as log_file: fnames = pup.fetch("store" + ext, processor=processor) logs = log_file.getvalue() lines = logs.splitlines() assert len(lines) == 2 assert lines[0].split()[0] == "Downloading" assert lines[-1].startswith(f"{msg} contents") assert len(fnames) == 2 assert true_paths == set(fnames) for fname in fnames: check_tiny_data(fname) # Check that processor doesn't execute when not downloading with capture_log() as log_file: fnames = pup.fetch("store" + ext, processor=processor) assert log_file.getvalue() == "" assert len(fnames) == 2 assert true_paths == set(fnames) for fname in fnames: check_tiny_data(fname)
import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.metrics import confusion_matrix from sklearn.metrics import accuracy_score from sklearn.metrics import classification_report from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import Imputer from sklearn.linear_model import LogisticRegression from sklearn.linear_model import LinearRegression dataset = pd.read_csv('E:/LINEARREGRESSION/Vijay/Titanic Dataset/INPUT/train.csv') test_data = pd.read_csv('E:/LINEARREGRESSION/Vijay/Titanic Dataset/INPUT/test.csv') gender_sub=pd.read_csv('E:/LINEARREGRESSION/Vijay/Titanic Dataset/INPUT/gender_submission.csv') gender_sub = gender_sub[['PassengerId', 'Survived']] X_gender_sub = gender_sub.iloc[:, [1]].values y_train = dataset.iloc[:, 1].values X_train = dataset.iloc[:, [2, 4, 5, 6]].values X_test = test_data.iloc[:, [1, 3, 4, 5]].values imp_mean = Imputer() imp_mean = imp_mean.fit(X_train[:, 2:4]) X_train[:, 2:4] = imp_mean.transform(X_train[:, 2:4]) imp_mean = imp_mean.fit(X_test[:, 2:4]) X_test[:, 2:4] = imp_mean.transform(X_test[:, 2:4]) labelencoder_x = LabelEncoder() X_train[:, 1] = labelencoder_x.fit_transform(X_train[:, 1].astype(str)) #X_train[:, 3] = labelencoder_x.fit_transform(X_train[:, 3].astype(str)) X_test[:, 1] = labelencoder_x.fit_transform(X_test[:, 1].astype(str)) #X_test[:, 3] = labelencoder_x.fit_transform(X_test[:, 3].astype(str)) ################################################################################################################################## # # Linear Regression # regressor = LinearRegression() # regressor.fit(X_train, y_train) # y_pred = regressor.predict(X_test) # final_pred = np.around(y_pred) # acc_lin = round(regressor.score(X_train, y_train) * 100, 2) # print('The score of the Linear regression is : ',acc_lin) # # Logistic Regression # logreg = LogisticRegression(penalty='l1',C=2.0,solver ='liblinear',max_iter=90) # logreg.fit(X_train, y_train) # Y_pred = logreg.predict(X_test) # final_pred=np.around(Y_pred) # acc_log = round(logreg.score(X_train, y_train) * 100, 2) # print('The score of the Logistic regression is : ',acc_log) # #Support Vector Machines # from sklearn.svm import SVC # svc = SVC(probability=True,gamma = 'scale',kernel='rbf') # svc.fit(X_train, y_train) # Y_pred = svc.predict(X_test) # final_pred=np.around(Y_pred) # acc_svc = round(svc.score(X_train, y_train) * 100, 2) # print('The score of the Support Vector Machine is : ',acc_svc) # #K-Nearest Neighbours # from sklearn.neighbors import KNeighborsClassifier # knn = KNeighborsClassifier(n_neighbors = 3) # knn.fit(X_train, y_train) # Y_pred = knn.predict(X_test) # final_pred=np.around(Y_pred) # acc_knn = round(knn.score(X_train, y_train) * 100, 2) # print('The score of K-Nearest Neighbours is : ',acc_knn) # #Gaussian Naive Bayes # from sklearn.naive_bayes import GaussianNB # gaussian = GaussianNB(priors=None, var_smoothing=1e-09) # gaussian.fit(X_train, y_train) # Y_pred = gaussian.predict(X_test) # final_pred=np.around(Y_pred) # acc_gaussian = round(gaussian.score(X_train, y_train) * 100, 2) # print('The score of Gaussian Naive Bayes Theorem is : ',acc_gaussian) # #Perceptron # from sklearn.linear_model import Perceptron # perceptron = Perceptron(alpha=0.0001, class_weight=None, early_stopping=False, eta0=1.0, # fit_intercept=True, max_iter=1000, n_iter_no_change=5, n_jobs=None, # penalty=None, random_state=0, shuffle=True, tol=0.001, # validation_fraction=0.1, verbose=0, warm_start=False) # perceptron.fit(X_train, y_train) # Y_pred = perceptron.predict(X_test) # final_pred=np.around(Y_pred) # acc_perceptron = round(perceptron.score(X_train, y_train) * 100, 2) # print('The score of Perceptron is : ', acc_perceptron) # #Linear SVM # from sklearn.svm import LinearSVC # linear_svc = LinearSVC(loss ='hinge',penalty='l2',C=2.0) # linear_svc.fit(X_train, y_train) # Y_pred = linear_svc.predict(X_test) # final_pred=np.around(Y_pred) # acc_linear_svc = round(linear_svc.score(X_train, y_train) * 100, 2) # print('The score of LinearSVC is : ', acc_linear_svc) # #Stochastic Gradient Descent # from sklearn.linear_model import SGDClassifier # sgd = SGDClassifier(loss="hinge", penalty="l2", max_iter=5) # sgd.fit(X_train, y_train) # Y_pred = sgd.predict(X_test) # final_pred=np.around(Y_pred) # acc_sgd = round(sgd.score(X_train, y_train) * 100, 2) # print('The score of Stochastic Gradient Descent is : ', acc_sgd) # #Decision Tree # from sklearn.tree import DecisionTreeClassifier # decision_tree = DecisionTreeClassifier(criterion = 'entropy',max_features=3,min_impurity_decrease=0,presort =True) # decision_tree.fit(X_train, y_train) # Y_pred = decision_tree.predict(X_test) # final_pred=np.around(Y_pred) # acc_decision_tree = round(decision_tree.score(X_train, y_train) * 100, 2) # print('The score of Decision Tree is : ', acc_decision_tree) #Random Forest from sklearn.ensemble import RandomForestClassifier random_forest = RandomForestClassifier(n_estimators=100,criterion = 'entropy',min_samples_leaf=2, min_samples_split=3,max_leaf_nodes=3,max_depth =10,min_weight_fraction_leaf=0.5, max_features=3,min_impurity_decrease=4,n_jobs =1,random_state=2) random_forest.fit(X_train, y_train) Y_pred = random_forest.predict(X_test) final_pred=np.around(Y_pred) random_forest.score(X_train, y_train) acc_random_forest = round(random_forest.score(X_train, y_train) * 100, 2) print('The score of Random Forest is : ',acc_random_forest) # Results results = confusion_matrix(X_gender_sub, final_pred) print ('Confusion Matrix :') print(results) print ('Accuracy Score :',accuracy_score(X_gender_sub, final_pred)) print ('Classification Report : ') print (classification_report(X_gender_sub, final_pred)) # imp_mean=Imputer(missing_values='NaN', strategy='mean',axis=1 ) #specify axis # imp_mean = imp_mean.fit(actual[:, [0, 1, 2, 3, 4]]) # actualdataset = imp_mean.transform(actual[:, [0, 1, 2, 3, 4]]) # # imp_means=Imputer(missing_values='NaN', strategy='mean',axis=1 ) # imp_mean = imp_mean.fit(predicted[:, [0, 1, 2]]) # predicteddataset = imp_mean.transform(predicted[:, [0, 1, 2]])
from autolens import exc import matplotlib.pyplot as plt import matplotlib.colors as colors import numpy as np import itertools from autolens.data.array.plotters import plotter_util def plot_array(array, origin=None, mask=None, extract_array_from_mask=False, zoom_around_mask=False, should_plot_border=False, positions=None, grid=None, as_subplot=False, units='arcsec', kpc_per_arcsec=None, figsize=(7, 7), aspect='equal', cmap='jet', norm='linear', norm_min=None, norm_max=None, linthresh=0.05, linscale=0.01, cb_ticksize=10, cb_fraction=0.047, cb_pad=0.01, title='Array', titlesize=16, xlabelsize=16, ylabelsize=16, xyticksize=16, mask_pointsize=10, border_pointsize=2, position_pointsize=30, grid_pointsize=1, xticks_manual=None, yticks_manual=None, output_path=None, output_format='show', output_filename='array'): """Plot an array of data as a figure. Parameters ----------- array : data.array.scaled_array.ScaledArray The 2D array of data which is plotted. origin : (float, float). The origin of the coordinate system of the array, which is plotted as an 'x' on the image if input. mask : data.array.mask.Mask The mask applied to the array, the edge of which is plotted as a set of points over the plotted array. extract_array_from_mask : bool The plotter array is extracted using the mask, such that masked values are plotted as zeros. This ensures \ bright features outside the mask do not impact the color map of the plot. zoom_around_mask : bool If True, the 2D region of the array corresponding to the rectangle encompassing all unmasked values is \ plotted, thereby zooming into the region of interest. should_plot_border : bool If a mask is supplied, its borders pixels (e.g. the exterior edge) is plotted if this is *True*. positions : [[]] Lists of (y,x) coordinates on the image which are plotted as colored dots, to highlight specific pixels. grid : data.array.grids.RegularGrid A grid of (y,x) coordinates which may be plotted over the plotted array. as_subplot : bool Whether the array is plotted as part of a subplot, in which case the grid figure is not opened / closed. units : str The units of the y / x axis of the plots, in arc-seconds ('arcsec') or kiloparsecs ('kpc'). kpc_per_arcsec : float or None The conversion factor between arc-seconds and kiloparsecs, required to plot the units in kpc. figsize : (int, int) The size of the figure in (rows, columns). aspect : str The aspect ratio of the array, specifically whether it is forced to be square ('equal') or adapts its size to \ the figure size ('auto'). cmap : str The colormap the array is plotted using, which may be chosen from the standard matplotlib colormaps. norm : str The normalization of the colormap used to plot the image, specifically whether it is linear ('linear'), log \ ('log') or a symmetric log normalization ('symmetric_log'). norm_min : float or None The minimum array value the colormap map spans (all values below this value are plotted the same color). norm_max : float or None The maximum array value the colormap map spans (all values above this value are plotted the same color). linthresh : float For the 'symmetric_log' colormap normalization ,this specifies the range of values within which the colormap \ is linear. linscale : float For the 'symmetric_log' colormap normalization, this allowws the linear range set by linthresh to be stretched \ relative to the logarithmic range. cb_ticksize : int The size of the tick labels on the colorbar. cb_fraction : float The fraction of the figure that the colorbar takes up, which resizes the colorbar relative to the figure. cb_pad : float Pads the color bar in the figure, which resizes the colorbar relative to the figure. xlabelsize : int The fontsize of the x axes label. ylabelsize : int The fontsize of the y axes label. xyticksize : int The font size of the x and y ticks on the figure axes. mask_pointsize : int The size of the points plotted to show the mask. border_pointsize : int The size of the points plotted to show the borders. positions_pointsize : int The size of the points plotted to show the input positions. grid_pointsize : int The size of the points plotted to show the grid. xticks_manual : [] or None If input, the xticks do not use the array's default xticks but instead overwrite them as these values. yticks_manual : [] or None If input, the yticks do not use the array's default yticks but instead overwrite them as these values. output_path : str The path on the hard-disk where the figure is output. output_filename : str The filename of the figure that is output. output_format : str The format the figue is output: 'show' - display on computer screen. 'png' - output to hard-disk as a png. 'fits' - output to hard-disk as a fits file.' Returns -------- None Examples -------- array_plotters.plot_array( array=image, origin=(0.0, 0.0), mask=circular_mask, extract_array_from_mask=True, zoom_around_mask=True, should_plot_border=False, positions=[[1.0, 1.0], [2.0, 2.0]], grid=None, as_subplot=False, units='arcsec', kpc_per_arcsec=None, figsize=(7,7), aspect='auto', cmap='jet', norm='linear, norm_min=None, norm_max=None, linthresh=None, linscale=None, cb_ticksize=10, cb_fraction=0.047, cb_pad=0.01, title='Image', titlesize=16, xlabelsize=16, ylabelsize=16, xyticksize=16, mask_pointsize=10, border_pointsize=2, position_pointsize=10, grid_pointsize=10, xticks_manual=None, yticks_manual=None, output_path='/path/to/output', output_format='png', output_filename='image') """ if array is None: return if extract_array_from_mask and mask is not None: array = np.add(array, 0.0, out=np.zeros_like(array), where=np.asarray(mask) == 0) if zoom_around_mask and mask is not None: array = array.extract_scaled_array_around_mask(mask=mask, buffer=2) plot_figure(array=array, as_subplot=as_subplot, units=units, kpc_per_arcsec=kpc_per_arcsec, figsize=figsize, aspect=aspect, cmap=cmap, norm=norm, norm_min=norm_min, norm_max=norm_max, linthresh=linthresh, linscale=linscale, xticks_manual=xticks_manual, yticks_manual=yticks_manual) plotter_util.set_title(title=title, titlesize=titlesize) set_xy_labels_and_ticksize(units=units, kpc_per_arcsec=kpc_per_arcsec, xlabelsize=xlabelsize, ylabelsize=ylabelsize, xyticksize=xyticksize) set_colorbar(cb_ticksize=cb_ticksize, cb_fraction=cb_fraction, cb_pad=cb_pad) plot_origin(array=array, origin=origin, units=units, kpc_per_arcsec=kpc_per_arcsec) plot_mask(mask=mask, units=units, kpc_per_arcsec=kpc_per_arcsec, pointsize=mask_pointsize) plot_border(mask=mask, should_plot_border=should_plot_border, units=units, kpc_per_arcsec=kpc_per_arcsec, pointsize=border_pointsize) plot_points(points_arc_seconds=positions, array=array, units=units, kpc_per_arcsec=kpc_per_arcsec, pointsize=position_pointsize) plot_grid(grid_arc_seconds=grid, array=array, units=units, kpc_per_arcsec=kpc_per_arcsec, pointsize=grid_pointsize) plotter_util.output_figure(array, as_subplot=as_subplot, output_path=output_path, output_filename=output_filename, output_format=output_format) plotter_util.close_figure(as_subplot=as_subplot) def plot_figure(array, as_subplot, units, kpc_per_arcsec, figsize, aspect, cmap, norm, norm_min, norm_max, linthresh, linscale, xticks_manual, yticks_manual): """Open a matplotlib figure and plot the array of data on it. Parameters ----------- array : data.array.scaled_array.ScaledArray The 2D array of data which is plotted. as_subplot : bool Whether the array is plotted as part of a subplot, in which case the grid figure is not opened / closed. units : str The units of the y / x axis of the plots, in arc-seconds ('arcsec') or kiloparsecs ('kpc'). kpc_per_arcsec : float or None The conversion factor between arc-seconds and kiloparsecs, required to plot the units in kpc. figsize : (int, int) The size of the figure in (rows, columns). aspect : str The aspect ratio of the array, specifically whether it is forced to be square ('equal') or adapts its size to \ the figure size ('auto'). cmap : str The colormap the array is plotted using, which may be chosen from the standard matplotlib colormaps. norm : str The normalization of the colormap used to plot the image, specifically whether it is linear ('linear'), log \ ('log') or a symmetric log normalization ('symmetric_log'). norm_min : float or None The minimum array value the colormap map spans (all values below this value are plotted the same color). norm_max : float or None The maximum array value the colormap map spans (all values above this value are plotted the same color). linthresh : float For the 'symmetric_log' colormap normalization ,this specifies the range of values within which the colormap \ is linear. linscale : float For the 'symmetric_log' colormap normalization, this allowws the linear range set by linthresh to be stretched \ relative to the logarithmic range. xticks_manual : [] or None If input, the xticks do not use the array's default xticks but instead overwrite them as these values. yticks_manual : [] or None If input, the yticks do not use the array's default yticks but instead overwrite them as these values. """ plotter_util.setup_figure(figsize=figsize, as_subplot=as_subplot) norm_min, norm_max = get_normalization_min_max(array=array, norm_min=norm_min, norm_max=norm_max) norm_scale = get_normalization_scale(norm=norm, norm_min=norm_min, norm_max=norm_max, linthresh=linthresh, linscale=linscale) extent = get_extent(array=array, units=units, kpc_per_arcsec=kpc_per_arcsec, xticks_manual=xticks_manual, yticks_manual=yticks_manual) plt.imshow(array, aspect=aspect, cmap=cmap, norm=norm_scale, extent=extent) def get_extent(array, units, kpc_per_arcsec, xticks_manual, yticks_manual): """Get the extent of the dimensions of the array in the units of the figure (e.g. arc-seconds or kpc). This is used to set the extent of the array and thus the y / x axis limits. Parameters ----------- array : data.array.scaled_array.ScaledArray The 2D array of data which is plotted. units : str The units of the y / x axis of the plots, in arc-seconds ('arcsec') or kiloparsecs ('kpc'). kpc_per_arcsec : float The conversion factor between arc-seconds and kiloparsecs, required to plot the units in kpc. xticks_manual : [] or None If input, the xticks do not use the array's default xticks but instead overwrite them as these values. yticks_manual : [] or None If input, the yticks do not use the array's default yticks but instead overwrite them as these values. """ if xticks_manual is not None and yticks_manual is not None: return np.asarray([xticks_manual[0], xticks_manual[3], yticks_manual[0], yticks_manual[3]]) if units in 'pixels': return np.asarray([0, array.shape[1], 0, array.shape[0]]) elif units in 'arcsec' or kpc_per_arcsec is None: return np.asarray([array.arc_second_minima[1], array.arc_second_maxima[1], array.arc_second_minima[0], array.arc_second_maxima[0]]) elif units in 'kpc': return list(map(lambda tick : tick*kpc_per_arcsec, np.asarray([array.arc_second_minima[1], array.arc_second_maxima[1], array.arc_second_minima[0], array.arc_second_maxima[0]]))) else: raise exc.PlottingException('The units supplied to the plotted are not a valid string (must be pixels | ' 'arcsec | kpc)') def get_normalization_min_max(array, norm_min, norm_max): """Get the minimum and maximum of the normalization of the array, which sets the lower and upper limits of the \ colormap. If norm_min / norm_max are not supplied, the minimum / maximum values of the array of data are used. Parameters ----------- array : data.array.scaled_array.ScaledArray The 2D array of data which is plotted. norm_min : float or None The minimum array value the colormap map spans (all values below this value are plotted the same color). norm_max : float or None The maximum array value the colormap map spans (all values above this value are plotted the same color). """ if norm_min is None: norm_min = array.min() if norm_max is None: norm_max = array.max() return norm_min, norm_max def get_normalization_scale(norm, norm_min, norm_max, linthresh, linscale): """Get the normalization scale of the colormap. This will be scaled based on the input min / max normalization \ values. For a 'symmetric_log' colormap, linthesh and linscale also change the colormap. If norm_min / norm_max are not supplied, the minimum / maximum values of the array of data are used. Parameters ----------- array : data.array.scaled_array.ScaledArray The 2D array of data which is plotted. norm_min : float or None The minimum array value the colormap map spans (all values below this value are plotted the same color). norm_max : float or None The maximum array value the colormap map spans (all values above this value are plotted the same color). linthresh : float For the 'symmetric_log' colormap normalization ,this specifies the range of values within which the colormap \ is linear. linscale : float For the 'symmetric_log' colormap normalization, this allowws the linear range set by linthresh to be stretched \ relative to the logarithmic range. """ if norm is 'linear': return colors.Normalize(vmin=norm_min, vmax=norm_max) elif norm is 'log': if norm_min == 0.0: norm_min = 1.e-4 return colors.LogNorm(vmin=norm_min, vmax=norm_max) elif norm is 'symmetric_log': return colors.SymLogNorm(linthresh=linthresh, linscale=linscale, vmin=norm_min, vmax=norm_max) else: raise exc.PlottingException('The normalization (norm) supplied to the plotter is not a valid string (must be ' 'linear | log | symmetric_log') def set_xy_labels_and_ticksize(units, kpc_per_arcsec, xlabelsize, ylabelsize, xyticksize): """Set the x and y labels of the figure, and set the fontsize of those labels. The x and y labels are always the distance scales, thus the labels are either arc-seconds or kpc and depend on the \ units the figure is plotted in. Parameters ----------- units : str The units of the y / x axis of the plots, in arc-seconds ('arcsec') or kiloparsecs ('kpc'). kpc_per_arcsec : float The conversion factor between arc-seconds and kiloparsecs, required to plot the units in kpc. xlabelsize : int The fontsize of the x axes label. ylabelsize : int The fontsize of the y axes label. xyticksize : int The font size of the x and y ticks on the figure axes. """ if units in 'pixels': plt.xlabel('x (pixels)', fontsize=xlabelsize) plt.ylabel('y (pixels)', fontsize=ylabelsize) elif units in 'arcsec' or kpc_per_arcsec is None: plt.xlabel('x (arcsec)', fontsize=xlabelsize) plt.ylabel('y (arcsec)', fontsize=ylabelsize) elif units in 'kpc': plt.xlabel('x (kpc)', fontsize=xlabelsize) plt.ylabel('y (kpc)', fontsize=ylabelsize) else: raise exc.PlottingException('The units supplied to the plotter are not a valid string (must be pixels | ' 'arcsec | kpc)') plt.tick_params(labelsize=xyticksize) def set_colorbar(cb_ticksize, cb_fraction, cb_pad): """Setup the colorbar of the figure, specifically its ticksize and the size is appears relative to the figure. Parameters ----------- cb_ticksize : int The size of the tick labels on the colorbar. cb_fraction : float The fraction of the figure that the colorbar takes up, which resizes the colorbar relative to the figure. cb_pad : float Pads the color bar in the figure, which resizes the colorbar relative to the figure. """ cb = plt.colorbar(fraction=cb_fraction, pad=cb_pad) cb.ax.tick_params(labelsize=cb_ticksize) def convert_grid_units(array, grid_arc_seconds, units, kpc_per_arcsec): """Convert the grid from its input units (arc-seconds) to the input unit (e.g. retain arc-seconds) or convert to \ another set of units (pixels or kilo parsecs). Parameters ----------- array : data.array.scaled_array.ScaledArray The 2D array of data which is plotted, the shape of which is used for converting the grid to units of pixels. grid_arc_seconds : ndarray or data.array.grids.RegularGrid The (y,x) coordinates of the grid in arc-seconds, in an array of shape (total_coordinates, 2). units : str The units of the y / x axis of the plots, in arc-seconds ('arcsec') or kiloparsecs ('kpc'). kpc_per_arcsec : float The conversion factor between arc-seconds and kiloparsecs, required to plot the units in kpc. """ if units in 'pixels': return array.grid_arc_seconds_to_grid_pixels(grid_arc_seconds=grid_arc_seconds) elif units in 'arcsec' or kpc_per_arcsec is None: return grid_arc_seconds elif units in 'kpc': return grid_arc_seconds * kpc_per_arcsec else: raise exc.PlottingException('The units supplied to the plotter are not a valid string (must be pixels | ' 'arcsec | kpc)') def plot_origin(array, origin, units, kpc_per_arcsec): """Plot the (y,x) origin ofo the array's coordinates as a 'x'. Parameters ----------- array : data.array.scaled_array.ScaledArray The 2D array of data which is plotted. origin : (float, float). The origin of the coordinate system of the array, which is plotted as an 'x' on the image if input. units : str The units of the y / x axis of the plots, in arc-seconds ('arcsec') or kiloparsecs ('kpc'). kpc_per_arcsec : float or None The conversion factor between arc-seconds and kiloparsecs, required to plot the units in kpc. """ if origin is not None: origin_grid = np.asarray(origin) origin_units = convert_grid_units(array=array, grid_arc_seconds=origin_grid, units=units, kpc_per_arcsec=kpc_per_arcsec) plt.scatter(y=origin_units[0], x=origin_units[1], s=80, c='k', marker='x') def plot_mask(mask, units, kpc_per_arcsec, pointsize): """Plot the mask of the array on the figure. Parameters ----------- mask : ndarray of data.array.mask.Mask The mask applied to the array, the edge of which is plotted as a set of points over the plotted array. units : str The units of the y / x axis of the plots, in arc-seconds ('arcsec') or kiloparsecs ('kpc'). kpc_per_arcsec : float or None The conversion factor between arc-seconds and kiloparsecs, required to plot the units in kpc. pointsize : int The size of the points plotted to show the mask. """ if mask is not None: plt.gca() edge_pixels = mask.masked_grid_index_to_pixel[mask.edge_pixels] + 0.5 edge_arc_seconds = mask.grid_pixels_to_grid_arc_seconds(grid_pixels=edge_pixels) edge_units = convert_grid_units(array=mask, grid_arc_seconds=edge_arc_seconds, units=units, kpc_per_arcsec=kpc_per_arcsec) plt.scatter(y=edge_units[:,0], x=edge_units[:,1], s=pointsize, c='k') def plot_border(mask, should_plot_border, units, kpc_per_arcsec, pointsize): """Plot the borders of the mask or the array on the figure. Parameters -----------t. mask : ndarray of data.array.mask.Mask The mask applied to the array, the edge of which is plotted as a set of points over the plotted array. should_plot_border : bool If a mask is supplied, its borders pixels (e.g. the exterior edge) is plotted if this is *True*. units : str The units of the y / x axis of the plots, in arc-seconds ('arcsec') or kiloparsecs ('kpc'). kpc_per_arcsec : float or None The conversion factor between arc-seconds and kiloparsecs, required to plot the units in kpc. border_pointsize : int The size of the points plotted to show the borders. """ if should_plot_border and mask is not None: plt.gca() border_pixels = mask.masked_grid_index_to_pixel[mask.border_pixels] border_arc_seconds = mask.grid_pixels_to_grid_arc_seconds(grid_pixels=border_pixels) border_units = convert_grid_units(array=mask, grid_arc_seconds=border_arc_seconds, units=units, kpc_per_arcsec=kpc_per_arcsec) plt.scatter(y=border_units[:,0], x=border_units[:,1], s=pointsize, c='y') def plot_points(points_arc_seconds, array, units, kpc_per_arcsec, pointsize): """Plot a set of points over the array of data on the figure. Parameters ----------- positions : [[]] Lists of (y,x) coordinates on the image which are plotted as colored dots, to highlight specific pixels. array : data.array.scaled_array.ScaledArray The 2D array of data which is plotted. units : str The units of the y / x axis of the plots, in arc-seconds ('arcsec') or kiloparsecs ('kpc'). kpc_per_arcsec : float or None The conversion factor between arc-seconds and kiloparsecs, required to plot the units in kpc. pointsize : int The size of the points plotted to show the input positions. """ if points_arc_seconds is not None: points_arc_seconds = list(map(lambda position_set: np.asarray(position_set), points_arc_seconds)) point_colors = itertools.cycle(["m", "y", "r", "w", "c", "b", "g", "k"]) for point_set_arc_seconds in points_arc_seconds: point_set_units = convert_grid_units(array=array, grid_arc_seconds=point_set_arc_seconds, units=units, kpc_per_arcsec=kpc_per_arcsec) plt.scatter(y=point_set_units[:,0], x=point_set_units[:,1], color=next(point_colors), s=pointsize) def plot_grid(grid_arc_seconds, array, units, kpc_per_arcsec, pointsize): """Plot a grid of points over the array of data on the figure. Parameters -----------. grid_arc_seconds : ndarray or data.array.grids.RegularGrid A grid of (y,x) coordinates in arc-seconds which may be plotted over the array. array : data.array.scaled_array.ScaledArray The 2D array of data which is plotted. units : str The units of the y / x axis of the plots, in arc-seconds ('arcsec') or kiloparsecs ('kpc'). kpc_per_arcsec : float or None The conversion factor between arc-seconds and kiloparsecs, required to plot the units in kpc. grid_pointsize : int The size of the points plotted to show the grid. """ if grid_arc_seconds is not None: grid_units = convert_grid_units(grid_arc_seconds=grid_arc_seconds, array=array, units=units, kpc_per_arcsec=kpc_per_arcsec) plt.scatter(y=np.asarray(grid_units[:, 0]), x=np.asarray(grid_units[:, 1]), s=pointsize, c='k')
from collections import OrderedDict from copy import deepcopy import json import re import datetime import logging from string import capitalize from datawinners.project.views.data_sharing import DataSharing from django.utils.safestring import mark_safe from django.utils.translation import ugettext_lazy as _, get_language from django.contrib.auth.decorators import login_required from django.http import HttpResponse, HttpResponseRedirect from django.shortcuts import render_to_response from django.template.context import RequestContext from django.utils.translation import ugettext from django.core.urlresolvers import reverse from django.views.decorators.csrf import csrf_view_exempt from elasticutils import F import jsonpickle from datawinners import settings from datawinners.accountmanagement.localized_time import get_country_time_delta, convert_utc_to_localized from datawinners.blue.xform_submission_exporter import XFormSubmissionExporter from datawinners.blue.view import SurveyWebXformQuestionnaireRequest from datawinners.blue.xform_bridge import XFormSubmissionProcessor from datawinners.project import helper from datawinners.accountmanagement.decorators import is_datasender, session_not_expired, is_not_expired, valid_web_user from datawinners.accountmanagement.models import NGOUserProfile from datawinners.common.authorization import is_data_sender from datawinners.feeds.database import get_feeds_database from datawinners.feeds.mail_client import mail_feed_errors from datawinners.main.database import get_database_manager from datawinners.monitor.carbon_pusher import send_to_carbon from datawinners.monitor.metric_path import create_path from datawinners.project.submission.exporter import SubmissionExporter from datawinners.project.submission.submission_search import get_submissions_paginated, \ get_all_submissions_ids_by_criteria, get_facets_for_choice_fields, get_submission_count, \ get_submissions_without_user_filters_count from datawinners.search.index_utils import es_questionnaire_field_name from datawinners.search.submission_headers import HeaderFactory from datawinners.search.submission_index import get_code_from_es_field_name from datawinners.search.submission_query import SubmissionQueryResponseCreator from mangrove.form_model.field import SelectField, DateField, UniqueIdField, FieldSet from mangrove.form_model.project import Project from mangrove.transport.player.new_players import WebPlayerV2 from datawinners.alldata.helper import get_visibility_settings_for from datawinners.custom_report_router.report_router import ReportRouter from datawinners.utils import get_organization from mangrove.form_model.form_model import get_form_model_by_code from mangrove.utils.dates import py_datetime_to_js_datestring from mangrove.utils.json_codecs import encode_json from datawinners.project.data_sender_helper import get_data_sender from datawinners.project.helper import SUBMISSION_DATE_FORMAT_FOR_SUBMISSION, is_project_exist from datawinners.project.utils import project_info, is_quota_reached from datawinners.project.Header import SubmissionsPageHeader from datawinners.activitylog.models import UserActivityLog from datawinners.common.constant import DELETED_DATA_SUBMISSION, EDITED_DATA_SUBMISSION from datawinners.project.views.utils import get_form_context, get_project_details_dict_for_feed, \ is_original_question_changed_from_choice_answer_type, is_original_field_and_latest_field_of_type_choice_answer, \ convert_choice_options_to_options_text, filter_submission_choice_options_based_on_current_answer_choices from datawinners.project.submission_form import SurveyResponseForm from mangrove.transport.repository.survey_responses import get_survey_response_by_id, get_survey_responses from mangrove.transport.contract.survey_response import SurveyResponse websubmission_logger = logging.getLogger("websubmission") logger = logging.getLogger("datawinners") @login_required @session_not_expired @is_datasender @is_not_expired def maps(request, form_code): manager = get_database_manager(request.user) submission_list = [] submissions = get_survey_responses(manager, form_code, None, None, view_name="undeleted_survey_response") for submission in submissions: submission_list.append({"complaint": submission.values["complaint"], "comment": submission.values["comment"], "long": submission.values["location"].split(',')[0], "lat": submission.values["location"].split(',')[1], "created": py_datetime_to_js_datestring(submission.created) }) return render_to_response('project/submission_map.html', {"submission_details":mark_safe(json.dumps(submission_list))}, context_instance=RequestContext(request)) @login_required @session_not_expired @is_datasender @is_not_expired def headers(request, form_code): manager = get_database_manager(request.user) submission_type = request.GET.get('type', 'all') form_model = get_form_model_by_code(manager, form_code) headers = SubmissionsPageHeader(form_model, submission_type).get_column_title() response = [] for header in headers: response.append({"sTitle": ugettext(header)}) return HttpResponse(encode_json(response)) def _get_date_fields_info(questionnaire): date_fields_array = [] for date_field in questionnaire.date_fields: date_fields_array.append({ 'code': date_field.code, 'label': date_field.label, 'is_month_format': date_field.is_monthly_format, 'format': date_field.date_format }) return date_fields_array def _is_unique_id_type_present(fields_array, unique_id_type): return len( [item for item in fields_array if item['type'] == 'unique_id' and item['entity_type'] == unique_id_type]) > 0 def get_filterable_field_details(field, filterable_fields): if isinstance(field, DateField): return { 'type': 'date', 'code': field.code, 'label': field.label, 'is_month_format': field.is_monthly_format, 'format': field.date_format } elif isinstance(field, UniqueIdField): if not _is_unique_id_type_present(filterable_fields, field.unique_id_type): return { 'type': 'unique_id', 'code': field.code, 'entity_type': field.unique_id_type, } def get_filterable_fields(fields): filterable_fields = [] for field in fields: field_detials = get_filterable_field_details(field, filterable_fields) if field_detials: filterable_fields.append(field_detials) if (isinstance(field, FieldSet) and field.is_group()): filterable_fields.extend(get_filterable_fields(field.fields)) return filterable_fields @login_required @session_not_expired @is_datasender @is_not_expired @is_project_exist def index(request, project_id=None, questionnaire_code=None, tab=0): manager = get_database_manager(request.user) org_id = helper.get_org_id_by_user(request.user) if request.method == 'GET': questionnaire = Project.get(manager, project_id) if questionnaire.is_void(): dashboard_page = settings.HOME_PAGE + "?deleted=true" return HttpResponseRedirect(dashboard_page) filterable_fields = get_filterable_fields(questionnaire.fields) first_filterable_fields = filterable_fields.pop(0) if filterable_fields else None xform = questionnaire.xform result_dict = { "user_email": request.user.email, "tab": tab, "xform": xform, "is_submission_exported_to_multiple_sheets": len(questionnaire.fields) > 253, # first 3 columns are additional submission data fields (ds_is, ds_name and submission_status) "is_quota_reached": is_quota_reached(request, org_id=org_id), "first_filterable_field": first_filterable_fields, "filterable_fields": filterable_fields, "is_media_field_present": questionnaire.is_media_type_fields_present, "is_simple_adv_form": not questionnaire.is_field_set_field_present() } result_dict.update(project_info(request, questionnaire, questionnaire_code)) return render_to_response('project/submission_results.html', result_dict, context_instance=RequestContext(request)) def _is_account_with_large_submissions(dbm): return dbm.database_name == 'hni_usaid-mikolo_lei526034' @login_required @session_not_expired @is_datasender @is_not_expired @is_project_exist def analysis_results(request, project_id=None, questionnaire_code=None): manager = get_database_manager(request.user) org_id = helper.get_org_id_by_user(request.user) if request.method == 'GET': questionnaire = Project.get(manager, project_id) dashboard_page = settings.HOME_PAGE + "?deleted=true" if questionnaire.is_void(): return HttpResponseRedirect(dashboard_page) filterable_fields = get_filterable_fields(questionnaire.fields) first_filterable_fields = filterable_fields.pop(0) if filterable_fields else None result_dict = { "xform": questionnaire.xform, "user_email": request.user.email, "is_quota_reached": is_quota_reached(request, org_id=org_id), "first_filterable_field": first_filterable_fields, "filterable_fields": filterable_fields, "is_submission_exported_to_multiple_sheets": len(questionnaire.fields) > 253, "is_media_field_present": questionnaire.is_media_type_fields_present, "is_simple_adv_form": not questionnaire.is_field_set_field_present() # first 3 columns are additional submission data fields (ds_is, ds_name and submission_status } result_dict.update(project_info(request, questionnaire, questionnaire_code)) return render_to_response('project/analysis_results.html', result_dict, context_instance=RequestContext(request)) def get_survey_response_ids_from_request(dbm, request, form_model, local_time_delta): if request.POST.get('all_selected', "false") == "true": search_filters = json.loads(request.POST.get("search_filters")) submission_type = request.POST.get("submission_type") search_parameters = {'filter': submission_type} search_parameters.update({'search_filters': search_filters}) return get_all_submissions_ids_by_criteria(dbm, form_model, search_parameters, local_time_delta) return json.loads(request.POST.get('id_list')) @is_project_exist def delete(request, project_id): dbm = get_database_manager(request.user) questionnaire = Project.get(dbm, project_id) dashboard_page = settings.HOME_PAGE + "?deleted=true" if questionnaire.is_void(): return HttpResponseRedirect(dashboard_page) organization = get_organization(request) local_time_delta = get_country_time_delta(organization.country) survey_response_ids = get_survey_response_ids_from_request(dbm, request, questionnaire, local_time_delta) received_times = [] for survey_response_id in survey_response_ids: survey_response = SurveyResponse.get(dbm, survey_response_id) received_times.append( datetime.datetime.strftime(convert_utc_to_localized(local_time_delta, survey_response.submitted_on), "%d/%m/%Y %X")) feeds_dbm = get_feeds_database(request.user) additional_feed_dictionary = get_project_details_dict_for_feed(questionnaire) delete_response = WebPlayerV2(dbm, feeds_dbm).delete_survey_response(survey_response, additional_feed_dictionary, websubmission_logger) mail_feed_errors(delete_response, dbm.database_name) if survey_response.data_record: ReportRouter().delete(get_organization(request).org_id, questionnaire.form_code, survey_response.data_record.id) if len(received_times): UserActivityLog().log(request, action=DELETED_DATA_SUBMISSION, project=questionnaire.name, detail=json.dumps({"Date Received": "[%s]" % ", ".join(received_times)})) response = encode_json({'success_message': ugettext("The selected records have been deleted"), 'success': True}) else: response = encode_json({'error_message': ugettext("No records deleted"), 'success': False}) return HttpResponse(response) def build_static_info_context(manager, survey_response, ui_model=None, questionnaire_form_model=None, reporter_id=None): form_ui_model = OrderedDict() if ui_model is None else ui_model sender_name, sender_id = get_data_sender(manager, survey_response)[:2] if sender_id == 'N/A': static_content = {'Data Sender': (survey_response.created_by, '')} else: static_content = {'Data Sender': (sender_name, sender_id)} static_content.update({'Source': capitalize( survey_response.channel) if survey_response.channel == 'web' else survey_response.channel.upper(), 'Submission Date': survey_response.submitted_on.strftime( SUBMISSION_DATE_FORMAT_FOR_SUBMISSION)}) form_ui_model.update({'static_content': static_content}) form_ui_model.update({'is_edit': True}) form_ui_model.update({'status': ugettext('Success') if survey_response.status else ugettext('Error')}) return form_ui_model def construct_request_dict(survey_response, questionnaire_form_model, short_code): result_dict = {} for field in questionnaire_form_model.fields: value = survey_response.values.get(field.code) if survey_response.values.get( field.code) else survey_response.values.get(field.code.lower()) original_field = questionnaire_form_model.get_field_by_code_and_rev(field.code, survey_response.form_model_revision) if is_original_question_changed_from_choice_answer_type(original_field, field): value = convert_choice_options_to_options_text(original_field, value) elif is_original_field_and_latest_field_of_type_choice_answer(original_field, field): value = filter_submission_choice_options_based_on_current_answer_choices(value, original_field, field) if isinstance(field, SelectField) and field.type == 'select': # check if select field answer is present in survey response value = re.findall(r'[1-9]?[a-z]', value) if value else value result_dict.update({field.code: value}) result_dict.update({'form_code': questionnaire_form_model.form_code}) result_dict.update({'dsid': short_code}) return result_dict @valid_web_user def edit_xform_submission_get(request, project_id, survey_response_id): survey_request = SurveyWebXformQuestionnaireRequest(request, project_id, XFormSubmissionProcessor()) if request.method == 'GET': return survey_request.response_for_xform_edit_get_request(survey_response_id) @valid_web_user @is_project_exist def edit(request, project_id, survey_response_id, tab=0): manager = get_database_manager(request.user) questionnaire_form_model = Project.get(manager, project_id) dashboard_page = settings.HOME_PAGE + "?deleted=true" reporter_id = NGOUserProfile.objects.get(user=request.user).reporter_id is_linked = reporter_id in questionnaire_form_model.data_senders reporter_name = NGOUserProfile.objects.get(user=request.user).user.first_name if questionnaire_form_model.is_void(): return HttpResponseRedirect(dashboard_page) disable_link_class, hide_link_class = get_visibility_settings_for(request.user) survey_response = get_survey_response_by_id(manager, survey_response_id) back_link = reverse(index, kwargs={"project_id": project_id, "questionnaire_code": questionnaire_form_model.form_code, "tab": tab}) form_ui_model = build_static_info_context(manager, survey_response, questionnaire_form_model=questionnaire_form_model, reporter_id=reporter_id) form_ui_model.update({"back_link": back_link, 'is_datasender': is_data_sender(request)}) data_sender = get_data_sender(manager, survey_response) short_code = data_sender[1] enable_datasender_edit = True if survey_response.owner_uid else False if request.method == 'GET': form_initial_values = construct_request_dict(survey_response, questionnaire_form_model, short_code) survey_response_form = SurveyResponseForm(questionnaire_form_model, form_initial_values, datasender_name=data_sender[0], reporter_id=reporter_id, reporter_name=reporter_name, enable_datasender_edit=enable_datasender_edit) form_ui_model.update(get_form_context(questionnaire_form_model, survey_response_form, manager, hide_link_class, disable_link_class)) form_ui_model.update({"redirect_url": "", "reporter_id": reporter_id, "is_linked": is_linked, "reporter_name": reporter_name}) if not survey_response_form.is_valid() or form_ui_model['datasender_error_message']: error_message = _("Please check your answers below for errors.") form_ui_model.update({'error_message': error_message, "reporter_id": reporter_id, "is_linked": is_linked, "reporter_name": reporter_name}) return render_to_response("project/web_questionnaire.html", form_ui_model, context_instance=RequestContext(request)) if request.method == 'POST': send_to_carbon(create_path('submissions.web.simple'), 1) original_survey_response = survey_response.copy() is_errored_before_edit = True if survey_response.errors != '' else False submitted_values = request.POST owner_id = submitted_values.get("dsid") form_ui_model.update({ "redirect_url": submitted_values.get("redirect_url"), 'is_datasender': is_data_sender(request) }) form_ui_model.update({"click_after_reload": submitted_values.get("click_after_reload")}) if submitted_values.get("discard"): survey_response_form = SurveyResponseForm(questionnaire_form_model, survey_response.values) form_ui_model.update( get_form_context(questionnaire_form_model, survey_response_form, manager, hide_link_class, disable_link_class)) form_ui_model.update({ "reporter_id": reporter_id, "is_linked": is_linked, "reporter_name": reporter_name}) return render_to_response("project/web_questionnaire.html", form_ui_model, context_instance=RequestContext(request)) else: form_initial_values = construct_request_dict(survey_response, questionnaire_form_model, short_code) if not owner_id: submitted_values = submitted_values.copy() submitted_values['dsid'] = form_initial_values['dsid'] survey_response_form = SurveyResponseForm(questionnaire_form_model, submitted_values, initial=form_initial_values, enable_datasender_edit=enable_datasender_edit) form_ui_model.update( get_form_context(questionnaire_form_model, survey_response_form, manager, hide_link_class, disable_link_class)) form_ui_model.update({ "reporter_id": reporter_id, "is_linked": is_linked}) if not survey_response_form.is_valid(): error_message = _("Please check your answers below for errors.") form_ui_model.update({'error_message': error_message, "reporter_id": reporter_id, "is_linked": is_linked}) return render_to_response("project/web_questionnaire.html", form_ui_model, context_instance=RequestContext(request)) success_message = _("Your changes have been saved.") form_ui_model.update({'success_message': success_message, "reporter_id": reporter_id, "is_linked": is_linked, "reporter_name": reporter_name}) # if len(survey_response_form.changed_data) or is_errored_before_edit: created_request = helper.create_request(survey_response_form, request.user.username) additional_feed_dictionary = get_project_details_dict_for_feed(questionnaire_form_model) user_profile = NGOUserProfile.objects.get(user=request.user) feeds_dbm = get_feeds_database(request.user) response = WebPlayerV2(manager, feeds_dbm, user_profile.reporter_id) \ .edit_survey_response(created_request, survey_response, owner_id, additional_feed_dictionary, websubmission_logger) mail_feed_errors(response, manager.database_name) if response.success: build_static_info_context(manager, survey_response, form_ui_model, questionnaire_form_model, reporter_id) ReportRouter().route(get_organization(request).org_id, response) _update_static_info_block_status(form_ui_model, is_errored_before_edit) log_edit_action(original_survey_response, survey_response, request, questionnaire_form_model.name, questionnaire_form_model) if submitted_values.get("redirect_url"): return HttpResponseRedirect(submitted_values.get("redirect_url")) else: del form_ui_model["success_message"] survey_response_form._errors = helper.errors_to_list(response.errors, questionnaire_form_model.fields) form_ui_model.update({ "reporter_id": reporter_id, "is_linked": is_linked, "reporter_name": reporter_name}) return render_to_response("project/web_questionnaire.html", form_ui_model, context_instance=RequestContext(request)) def log_edit_action(old_survey_response, new_survey_response, request, project_name, form_model): differences = new_survey_response.differs_from(old_survey_response) diff_dict = {} changed_answers = deepcopy(differences.changed_answers) if differences.changed_answers: for key, value in differences.changed_answers.iteritems(): question_field = form_model.get_field_by_code(key) question_label = question_field.label # replacing question code with actual question text changed_answers[question_label] = changed_answers.pop(key) # relace option with value for choice field if isinstance(question_field, SelectField): changed_answers[question_label] = get_option_value_for_field(value, question_field) diff_dict.update({'changed_answers': changed_answers}) diff_dict.update({'received_on': differences.created.strftime(SUBMISSION_DATE_FORMAT_FOR_SUBMISSION)}) diff_dict.update({'status_changed': differences.status_changed}) activity_log = UserActivityLog() activity_log.log(request, project=project_name, action=EDITED_DATA_SUBMISSION, detail=json.dumps(diff_dict)) def formatted_field_value_for_excel(diff_value, question_field): prev_choice_values = question_field.formatted_field_values_for_excel(diff_value) return prev_choice_values def get_option_value_for_field(diff_value, question_field): prev_choice_values = formatted_field_value_for_excel(diff_value["old"], question_field) reslt_dict = {"old": ', '.join(prev_choice_values) if prev_choice_values else diff_value["old"], "new": ', '.join(formatted_field_value_for_excel(diff_value["new"], question_field))} return reslt_dict @login_required @session_not_expired @is_datasender @is_not_expired def export_count(request): if request.method == 'GET': return HttpResponse(status=405) submission_type = request.GET.get(u'type') post_body = json.loads(request.POST['data']) search_filters = post_body['search_filters'] questionnaire_code = post_body['questionnaire_code'] manager = get_database_manager(request.user) form_model = get_form_model_by_code(manager, questionnaire_code) organization = get_organization(request) local_time_delta = get_country_time_delta(organization.country) # the number_of_results limit will not be used for result-set size since scan-scroll api does not support it. #it is specified since the code-flow requires its value to be present query_params = {"search_filters": search_filters, "start_result_number": 0, "number_of_results": 4000, "order": "", "sort_field": "date" } search_text = search_filters.get("search_text", '') query_params.update({"search_text": search_text}) query_params.update({"filter": submission_type}) submission_count = get_submission_count(manager, form_model, query_params, local_time_delta) return HttpResponse(mimetype='application/json', content=json.dumps({"count": submission_count})) @login_required @session_not_expired @is_datasender @is_not_expired def export(request): if request.method == 'GET': # To handle django error #3480 return HttpResponse(status=405) project_name = request.POST.get(u"project_name") submission_type = request.GET.get(u'type') search_filters = json.loads(request.POST.get('search_filters')) questionnaire_code = request.POST.get(u'questionnaire_code') manager = get_database_manager(request.user) form_model = get_form_model_by_code(manager, questionnaire_code) current_language = get_language() organization = get_organization(request) local_time_delta = get_country_time_delta(organization.country) # the number_of_results limit will not be used for result-set size since scan-scroll api does not support it. #it is specified since the code-flow requires its value to be present query_params = {"search_filters": search_filters, "start_result_number": 0, "number_of_results": 4000, "order": "", "sort_field": "date" } search_text = search_filters.get("search_text", '') query_params.update({"search_text": search_text}) query_params.update({"filter": submission_type}) is_media = False if request.POST.get('is_media') == u'true': is_media = True if form_model.xform: if not is_media: return XFormSubmissionExporter(form_model, project_name, manager, local_time_delta, current_language) \ .create_excel_response_without_zip(submission_type, query_params) else: return XFormSubmissionExporter(form_model, project_name, manager, local_time_delta, current_language) \ .create_excel_response_with_media(submission_type, query_params) return SubmissionExporter(form_model, project_name, manager, local_time_delta, current_language) \ .create_excel_response_without_zip(submission_type, query_params) def _update_static_info_block_status(form_model_ui, is_errored_before_edit): if is_errored_before_edit: form_model_ui.update({'is_error_to_success': is_errored_before_edit}) form_model_ui['status'] = ugettext('Success') def _get_field_to_sort_on(post_dict, form_model, filter_type): order_by = int(post_dict.get('iSortCol_0')) - 1 header = HeaderFactory(form_model).create_header(filter_type) headers = header.get_header_field_names() meta_fields = ['ds_id', 'entity_short_code'] for field in meta_fields: # Remove extra meta fields with which ordering in submission values # and submission headers will not match try: headers.remove(field) except ValueError: pass return headers[order_by] @csrf_view_exempt @valid_web_user def get_submissions(request, form_code): dbm = get_database_manager(request.user) form_model = get_form_model_by_code(dbm, form_code) search_parameters = {} search_parameters.update({"start_result_number": int(request.POST.get('iDisplayStart'))}) search_parameters.update({"number_of_results": int(request.POST.get('iDisplayLength'))}) filter_type = request.GET['type'] search_parameters.update({"filter": filter_type}) search_parameters.update({"sort_field": _get_field_to_sort_on(request.POST, form_model, filter_type)}) search_parameters.update({"order": "-" if request.POST.get('sSortDir_0') == "desc" else ""}) search_filters = json.loads(request.POST.get('search_filters')) DataSharing(form_model, request.user).append_data_filter(search_filters) search_parameters.update({"search_filters": search_filters}) search_text = search_filters.get("search_text", '') search_parameters.update({"search_text": search_text}) organization = get_organization(request) local_time_delta = get_country_time_delta(organization.country) search_results, query_fields = get_submissions_paginated(dbm, form_model, search_parameters, local_time_delta) submission_count_with_filters = get_submission_count(dbm, form_model, search_parameters, local_time_delta) submission_count_without_filters = get_submissions_without_user_filters_count(dbm, form_model, search_parameters) submissions = SubmissionQueryResponseCreator(form_model, local_time_delta).create_response(query_fields, search_results) return HttpResponse( jsonpickle.encode( { 'data': submissions, 'iTotalDisplayRecords': submission_count_with_filters, 'iDisplayStart': int(request.POST.get('iDisplayStart')), "iTotalRecords": submission_count_without_filters, 'iDisplayLength': int(request.POST.get('iDisplayLength')) }, unpicklable=False), content_type='application/json') def get_facet_response_for_choice_fields(query_with_criteria, choice_fields, form_model_id): facet_results = [] for field in choice_fields: field_name = es_questionnaire_field_name(field.code, form_model_id) + "_exact" facet_response = query_with_criteria.facet(field_name, filtered=True).facet_counts() facet_result_options = [] facet_result = { "es_field_name": field_name, "facets": facet_result_options, # find total submissions containing specified answer "total": query_with_criteria.filter(~F(**{field_name: None})).count() } for option, facet_list in facet_response.iteritems(): for facet in facet_list: facet_result_options.append({ "term": facet['term'], "count": facet['count'] }) facet_results.append(facet_result) return facet_results @csrf_view_exempt @valid_web_user def get_stats(request, form_code): dbm = get_database_manager(request.user) form_model = get_form_model_by_code(dbm, form_code) search_parameters = {} search_parameters.update({"start_result_number": 0}) search_parameters.update({"number_of_results": 0}) filter_type = "success" search_parameters.update({"filter": filter_type}) search_parameters.update({"sort_field": "ds_id"}) search_parameters.update({"order": "-" if request.POST.get('sSortDir_0') == "desc" else ""}) search_filters = json.loads(request.POST.get('search_filters')) search_parameters.update({"search_filters": search_filters}) search_text = search_filters.get("search_text", '') search_parameters.update({"search_text": search_text}) organization = get_organization(request) local_time_delta = get_country_time_delta(organization.country) # total success submission count irrespective of current fields being present or not facet_results, total_submissions = get_facets_for_choice_fields(dbm, form_model, search_parameters, local_time_delta) return HttpResponse(json.dumps( {'result': create_statistics_response(facet_results, form_model), 'total': total_submissions }), content_type='application/json') def create_statistics_response(facet_results, form_model): analysis_response = OrderedDict() for facet_result in facet_results: field_code = get_code_from_es_field_name(facet_result['es_field_name'], form_model.id) field = form_model.get_field_by_code(field_code) field_options = [option['text'] for option in field.options] facet_result_options = [] facet_terms = [] for facet in facet_result['facets']: facet_result_options.append({'term': facet['term'], 'count': facet['count']}) facet_terms.append(facet['term']) for option in field_options: if option not in facet_terms: facet_result_options.append({'term': option, 'count': 0}) analysis_response.update({field.label: { 'data': facet_result_options, 'field_type': field.type, 'count': facet_result['total'] } }) return analysis_response
'''Game main module. Contains the entry point used by the run_game.py script. Feel free to put all your game code here, or in other modules in this "gamelib" package. ''' import pygame import pygame.time import pygame.display import pygame.event import os import math import moderngl import array import numpy from gamelib.terrain import Terrain from . import data import OpenGL import OpenGL.arrays import OpenGL.arrays.vbo from OpenGL.GL import * from OpenGL.GLU import * from OpenGL.GL import shaders #from OpenGL.arrays import vbo from .draw_text import * from .cube import * from .vbo import * from .myshader import * from . import guns from . import terrain import random import time import glm DPF = 0 def mat2list(m): return numpy.array([i for v in m for i in v ], 'f') def dprint(x): s='' for i in dir(x): if i.startswith('_'): continue try: s+='{}:{} \n'.format(i, getattr(x,i)) except: s+='{}:{} \n'.format(i, 'error') print(s) class KeyNames: def __init__(self): self.names = { 1 : 'M_LEFT', 2 : 'M_CENTER', 3 : 'M_RIGHT', } for k in dir(pygame): if k.startswith('K_'): n = getattr(pygame, k) self.names[n] = k def get(self, n): return self.names.get(n) or "{}".format(n) class Monster: pos = glm.vec3() vel = glm.vec3() dir = glm.vec3() yaw = 0 pitch = 0 hp = 100 gun = None def __init__(self): self.inputs = {} self.guns = [] def calc_dir(self): self.dir = glm.vec3(glm.cos(self.yaw) * glm.cos(self.pitch), glm.sin(self.yaw) * glm.cos(self.pitch), glm.sin(self.pitch)) class Ray: start = glm.vec3() end = glm.vec3() vel = glm.vec3() color = glm.vec3() ttl = 0 # frames size = 0.05 class Rays: def __init__(self): self.rays = [] def process(self): for r in self.rays: r.start += r.vel r.end += r.vel for r in self.rays[:]: if r.ttl - 1 == 0: self.rays.remove(r) else: r.ttl -= 1 def draw(self, game): Texture.set(data.filepath('fruit.png')) if not self.rays: return arr = [] parr = [arr] def add_with_offset(r, off, c): s = r + off parr[0] += [s.x, s.y, s.z, 0, 0, c.x, c.y, c.z] for r in self.rays: add_with_offset(r.start, glm.vec3(0,0,-r.size), r.color) add_with_offset(r.end, glm.vec3(0,0,-r.size), r.color) add_with_offset(r.end, glm.vec3(0,0,r.size), r.color) add_with_offset(r.start, glm.vec3(0, 0, -r.size), r.color) add_with_offset(r.start, glm.vec3(0, 0, r.size), r.color) add_with_offset(r.end, glm.vec3(0, 0, r.size), r.color) add_with_offset(r.start, glm.vec3(0, -r.size, 0), r.color) add_with_offset(r.end, glm.vec3(0, -r.size, 0), r.color) add_with_offset(r.end, glm.vec3(0, r.size, 0), r.color) add_with_offset(r.start, glm.vec3(0, -r.size, 0), r.color) add_with_offset(r.start, glm.vec3(0, r.size, 0), r.color) add_with_offset(r.end, glm.vec3(0, r.size, 0), r.color) vb = Vbo.get(data=arr) glUniformMatrix4fv( glGetUniformLocation(game.planet_shader, 'u_proj'), 1, False, mat2list(glm.perspective(glm.radians(game.fov), game.aspect, 0.1, 1000.0))) glUniformMatrix4fv( glGetUniformLocation(game.planet_shader, 'u_view'), 1, False, mat2list(glm.lookAt( game.player.pos, game.player.pos + game.player.dir, glm.vec3(0, 0, 1)))) glUniform1i(gul(game.planet_shader, 'sampler'), 0) glEnableVertexAttribArray(gal(game.planet_shader, 'in_vert')) with vb.vbo: glVertexAttribPointer(gal(game.planet_shader, 'in_vert'), 3, GL_FLOAT, False, 8*4, vb.vbo) glDrawArrays(GL_TRIANGLES, 0, vb.count) global DPF DPF += 1 vb.vbo.delete() class Model: models = {} name = '' texture_name = '' shader_name = '' data_name = '' class Box: ix = 0 iy = 0 iz = 0 # air, floor, crate, wall, stairup, stairdown==air, floor+enemy, floor+player t = '' zscale = 1.0 xyscale = 1.0 texture = 'road.png' def __init__(self): pass def draw(self, game,tower): def gal(s): return glGetAttribLocation(game.planet_shader, s) game.shell.texture.set(data.filepath(self.texture)) mview = glm.lookAt( game.player.pos, game.player.pos + game.player.dir, glm.vec3(0, 0, 1)) mpos = glm.translate(mview, glm.vec3(tower.pos.x+self.xyscale*self.ix, tower.pos.y+self.xyscale*self.iy, tower.pos.z+tower.hceil*self.iz)) mscl = glm.scale(mpos, glm.vec3(self.xyscale, self.xyscale, self.zscale)) #mfixbox1 = glm.translate(mscl, glm.vec3(1, 1, 0)) #mfixbox2 = glm.scale(mfixbox1, glm.vec3(0.5, 0.5, 1)) glUniformMatrix4fv( glGetUniformLocation(game.planet_shader, 'u_view'), 1, False, mat2list(mscl)) glDrawArrays(GL_TRIANGLES, 0, game.vbo_cube.count) global DPF DPF += 1 tower1 = ''' -------- XX.#.... XX...... XXX..X.. X^...XX. X^....X. XXXX..X@ XXXX.... XX.@.... -------- ...///// ...///// ...///// .v////// .v////// ..////^^ ........ .XX..... -------- ........ .X...... .X..//.. .XX.//.. @@X.//vv ..@.XXXX ....XXXX ''' def yxrange(h, w): for y in range(h): for x in range(w): yield y, x class Tower: def __init__(self): self.boxes = [] self.w = 8 self.h = 8 self.hceil = 4.0 self.pos = glm.vec3(0,0,100) for c in range(3): for b, a in yxrange(self.h, self.w): box = Box() box.ix = a box.iy = b box.iz = c if b in (0,7): box.zscale = self.hceil else: box.zscale = 0.1 if 1 < a < 7 else 1 self.boxes.append(box) def draw(self, game): with game.vbo_cube.vbo: glVertexAttribPointer(0, 3, GL_FLOAT, False, 8 * 4, game.vbo_cube.vbo) for i in self.boxes: i.draw(game, self) class Game: def __init__(self, shell): self.key_names = KeyNames() self.shell = shell self.aim = False self.player = Monster() self.player.guns.append(guns.Pistol()) self.player.guns.append(guns.Shotgun()) self.player.guns.append(guns.SMG()) self.player.guns.append(guns.Rifle()) self.player.guns.append(guns.SniperRifle()) self.player.guns.append(guns.MachineGun()) self.player.gun = self.player.guns[0] self.fov = 90 self.tgt_fov = 90 self.aspect = 4.0/3.0 self.player.pos = glm.vec3(300,80,130) self.planet_shader = shaders.compileProgram( shaders.compileShader(data.load_text('planet.vert'), GL_VERTEX_SHADER), shaders.compileShader(data.load_text('planet.frag'), GL_FRAGMENT_SHADER), ) self.towers = [] t = Tower() t.pos = glm.vec3(300, 80, 100) self.towers.append(t) self.terrain = terrain.Terrain('terrain.png') self.vbo_planet = Vbo.get('planet', self.terrain.make_data()) self.vbo_cube = Vbo.get('cube', make_cube()) self.rays = Rays() r = Ray() r.start = glm.vec3(0, 0, 0) r.end = glm.vec3(100, 100, 100) self.rays.rays.append(r) r = Ray() r.start = glm.vec3(300, 80, 108) r.end = glm.vec3(400, 80, 108) self.rays.rays.append(r) def on_key(self, key, is_down): print("key {} is {}".format(self.key_names.get(key), 'down' if is_down else 'up')) self.player.inputs[key] = is_down if key == 3: self.aim = is_down if self.aim: self.tgt_fov = 5 else: self.tgt_fov = 90 def fire_weapon(self, m, g): if g.charges < 1.0: return False if g.temperature >= g.fail_temperature: return False # create rays for i in range(g.rays_per_shot): r = Ray() up = glm.vec3(0, 0, 1) side = glm.normalize(glm.cross(m.dir, glm.vec3(0, 0, 1))) identity = glm.identity(glm.mat4) xspread = random.randint(-100, 100) + random.randint(-100, 100) yspread = random.randint(-100, 100) + random.randint(-100, 100) xspread = (0.01 * xspread) * g.spreadx + g.offsetx yspread = (0.01 * yspread) * g.spready + g.offsety spreaded_dir = ( glm.rotate(identity, glm.radians(xspread), up) * glm.vec4( m.dir, 0.0)).xyz spreaded_dir = ( glm.rotate(identity, glm.radians(yspread), side) * glm.vec4( spreaded_dir, 0.0)).xyz gun_offset = 0.1 r.start = m.pos + spreaded_dir * 0.5 + glm.vec3(0, 0, -gun_offset) + side * gun_offset r.end = m.pos + spreaded_dir * 300 + glm.vec3(0, 0, 0) r.vel = spreaded_dir * 0.3 r.ttl = 30 self.rays.rays.append(r) print('{}'.format(r.start)) # add spread, temp and decrease charges g.spreadx += g.spreadx_per_shot g.spready += g.spready_per_shot g.offsetx += g.offsetx_per_shot g.offsety += g.offsety_per_shot g.charges -= 1.0 g.temperature += g.dt_per_shot return True def process_weapon(self, m): if not m.gun: return assert isinstance(m.gun, guns.Gun) g = m.gun ''' # state: charges = 10.0 spread = 1.0 offsetx = 0.0 offsety = 0.0 temperature = 0.0 state_shot = 0 # no of frame when shooting initiated state_reload = 0 # no of frame when reloading initiated ''' initiate_fire = False if m.inputs.get(1): if g.state_reload: pass elif g.state_shot: if g.state_shot == g.auto_shot_time: initiate_fire = True else: initiate_fire = True if initiate_fire: g.state_shot = 1 # decay spread, temp g.spreadx = g.spreadx * g.spread_decay + g.normal_spread * (1.0 - g.spread_decay) g.spready = g.spready * g.spread_decay + g.normal_spread * (1.0 - g.spread_decay) g.offsetx = g.offsetx * g.spread_decay g.offsety = g.offsety * g.spread_decay g.temperature *= g.temperature_decay if g.state_shot == g.preshot_time: if not self.fire_weapon(m, g): g.state_shot = 0 elif g.state_shot >= g.shot_time: g.state_shot = 0 if g.state_shot: g.state_shot += 1 if g.state_reload: g.state_reload += 1 def process_monster(self, m): assert isinstance(m, Monster) mvel = 0.1 if m.inputs.get(pygame.K_w): m.pos += m.dir * mvel if m.inputs.get(pygame.K_s): m.pos -= m.dir * mvel if m.inputs.get(pygame.K_a): side = glm.normalize(glm.cross(m.dir, glm.vec3(0,0,1))) m.pos -= side * mvel if m.inputs.get(pygame.K_d): side = glm.normalize(glm.cross(m.dir, glm.vec3(0, 0, 1))) m.pos += side * mvel if m.inputs.get(pygame.K_SPACE): m.pos.z += mvel if m.inputs.get(pygame.K_c): m.pos.z -= mvel for i in range(1,9): if m.inputs.get(pygame.K_1 - 1 + i): if i-1 < len(m.guns): m.gun = m.guns[i-1] self.process_weapon(m) zr = 0.5 terrainz = max([self.terrain.getz(m.pos.x, m.pos.y), self.terrain.getz(m.pos.x+zr, m.pos.y), self.terrain.getz(m.pos.x-zr, m.pos.y), self.terrain.getz(m.pos.x, m.pos.y+zr), self.terrain.getz(m.pos.x, m.pos.y-zr)]) #m.pos.z = terrainz + 1.7 #print('{:.2f} {:.2f}'.format(terrainz, self.terrain.getz(m.pos.x, m.pos.y))) if m.pos.z > terrainz + 1.7: # in air m.vel.z -= 0.01 elif m.pos.z > terrainz - 1.7: # hit ground desiredz = terrainz + 1.7 delta = desiredz - m.pos.z m.pos.z = m.pos.z * 0.7 + desiredz * 0.3 if m.vel.z < 0: m.vel.z = 0 else: pass m.pos += m.vel m.vel *= 0.99 prev_pos = None def on_move(self, position): #print(position) if self.prev_pos is None: self.prev_pos = glm.vec2(position) pos = glm.vec2(position) dpos = self.prev_pos - pos #print(dpos) sens = 0.0001 * self.fov #if self.aim: # sens = 0.001 self.player.yaw -= sens * position[0] self.player.pitch -= sens * position[1] if self.player.pitch < -1.57: self.player.pitch = -1.57 if self.player.pitch > 1.57: self.player.pitch = 1.57 self.prev_pos = pos self.player.calc_dir() def draw(self): self.process_monster(self.player) self.fov = self.fov * 0.92 + self.tgt_fov * 0.08 Texture.set(data.filepath('planet.png')) glUseProgram(self.planet_shader) def gul(s): return glGetUniformLocation(self.planet_shader, s) def gal(s): return glGetAttribLocation(self.planet_shader, s) glUniformMatrix4fv( glGetUniformLocation(self.planet_shader, 'u_proj'), 1, False, mat2list(glm.perspective(glm.radians(self.fov), self.aspect, 0.1, 1000.0))) glUniformMatrix4fv( glGetUniformLocation(self.planet_shader, 'u_view'), 1, False, mat2list(glm.lookAt( self.player.pos, self.player.pos + self.player.dir, glm.vec3(0, 0, 1)))) glUniform1i(gul('sampler'), 0) glEnableVertexAttribArray(gal('in_vert')) with self.vbo_planet.vbo: glVertexAttribPointer(gal('in_vert'), 3, GL_FLOAT, False, 8*4, self.vbo_planet.vbo) glDrawArrays(GL_TRIANGLES, 0, self.vbo_planet.count) global DPF DPF += 1 Texture.set(data.filepath('water.png')) glUniformMatrix4fv( glGetUniformLocation(self.planet_shader, 'u_view'), 1, False, mat2list( glm.scale( glm.translate( glm.lookAt( self.player.pos, self.player.pos + self.player.dir, glm.vec3(0, 0, 1)), glm.vec3(0, 0, 100.0)), glm.vec3(1, 1, 0)))) with self.vbo_planet.vbo: glVertexAttribPointer(gal('in_vert'), 3, GL_FLOAT, False, 8*4, self.vbo_planet.vbo) glDrawArrays(GL_TRIANGLES, 0, self.vbo_planet.count) global DPF DPF += 1 self.rays.process() self.rays.draw(self) #for t in self.towers: # t.draw(self) glUseProgram(0) class Shell: def __init__(self): self.text = TextDrawer() self.crosshair = CrosshairDrawer() self.texture = Texture() self.grabbed = True self.overdraw = 1 pygame.init() pygame.display.init() pygame.display.set_mode([1024, 768], pygame.OPENGL | pygame.DOUBLEBUF | pygame.RESIZABLE) self.clock = pygame.time.Clock() self.text.init() self.crosshair.init() self.game = Game(self) # workaround for pygame.mixer.quit() pygame.event.set_grab(self.grabbed) pygame.mouse.set_visible(not self.grabbed) def resize(self, w, h): pygame.display.set_mode([w, h], pygame.OPENGL | pygame.DOUBLEBUF | pygame.RESIZABLE) glViewport(0, 0, w, h) self.aspect = float(w) / h def draw(self): glClearColor(0.3, 0.4, 0.5, 1.0) glClear(GL_COLOR_BUFFER_BIT|GL_DEPTH_BUFFER_BIT) #glEnable(GL_CULL_FACE) glEnable(GL_DEPTH_TEST) fps = self.clock.get_fps() global DPF s = "FPS: {:.1f} * {} {}".format( fps, self.overdraw, self.game.player.gun.str() ) DPF = 0 self.text.draw((10, 570), s, 30) self.crosshair.draw(0, data.filepath('crosshair.png'), 1/self.game.aspect) for i in range(self.overdraw): self.game.draw() glFinish() glFlush() pygame.display.flip() def start(self): while True: dt=self.clock.tick(60) for e in pygame.event.get(): if e.type == pygame.QUIT: return elif e.type == pygame.KEYDOWN and e.key == pygame.K_ESCAPE: return elif e.type == pygame.KEYDOWN: self.game.on_key(e.key, is_down=True) if e.key == pygame.K_g: self.grabbed = not self.grabbed pygame.event.set_grab(self.grabbed) pygame.mouse.set_visible(not self.grabbed) if e.key in (pygame.K_EQUALS, pygame.K_PLUS): self.overdraw += 1 if e.key == pygame.K_MINUS: self.overdraw = max(self.overdraw - 1, 1) elif e.type == pygame.KEYUP: self.game.on_key(e.key, is_down=False) elif e.type == pygame.MOUSEMOTION: self.game.on_move(e.rel) elif e.type == pygame.MOUSEBUTTONDOWN: self.game.on_key(e.button, is_down=True) elif e.type == pygame.MOUSEBUTTONUP: self.game.on_key(e.button, is_down=False) elif e.type == pygame.VIDEORESIZE: self.resize(e.w, e.h) else: print(e) self.draw() def main(): g = Shell() g.start() print("Thank you for playing.")
<filename>snmp/nav/smidumps/NetPing_DKSF_60_5_2_MB_mib.py<gh_stars>0 # python version 1.0 DO NOT EDIT # # Generated by smidump version 0.4.8: # # smidump -f python DKSF-60-4-X-X-X FILENAME = "mibs/NetPing/[Pub] DKSF 60.5.2 MB.mib" MIB = { "moduleName" : "DKSF-60-4-X-X-X", "DKSF-60-4-X-X-X" : { "nodetype" : "module", "language" : "SMIv2", "organization" : """NetPing East, Alentis Electronics""", "contact" : """<EMAIL>""", "description" : """Generic MIB for NetPing remote sensing and control""", "revisions" : ( { "date" : "2016-01-14 00:00", "description" : """npIrStatus values changed""", }, { "date" : "2015-09-24 00:00", "description" : """npElecMeter branch added""", }, { "date" : "2015-04-09 00:00", "description" : """npRelHumTrap added; npIoTrapLevelLegend added""", }, { "date" : "2014-11-21 00:00", "description" : """npRelayFlip variable max-access bugfix""", }, { "date" : "2014-10-23 00:00", "description" : """Major update for DKSF 60.4""", }, { "date" : "2012-10-09 00:00", "description" : """npTrapEmailTo variable is added in Traps for forwarding to email with external service""", }, { "date" : "2012-03-01 00:00", "description" : """npReboot branch is added""", }, { "date" : "2011-04-11 00:00", "description" : """npIoPulseCounter, npIoSinglePulseDuration, npIoSinglePulseStart is added Identification is changed from DKSF 52.3 to DKSF 52.4""", }, { "date" : "2011-02-05 00:00", "description" : """npIoCurLoop Traps""", }, { "date" : "2011-01-28 00:00", "description" : """DKSF 50.9.1.A-2,-3,-10 - IR module (DKST28) support, new Trap definitions for npIO and npThermo. It's NO backward compatibility with DKSF 50.8 on Traps!""", }, { "date" : "2010-04-14 00:00", "description" : """SMIv2-style rewrite""", }, ), "identity node" : "netPing", }, "imports" : ( {"module" : "SNMPv2-SMI", "name" : "enterprises"}, {"module" : "SNMPv2-SMI", "name" : "MODULE-IDENTITY"}, {"module" : "SNMPv2-SMI", "name" : "OBJECT-TYPE"}, {"module" : "SNMPv2-SMI", "name" : "Counter32"}, {"module" : "SNMPv2-SMI", "name" : "Gauge32"}, {"module" : "SNMPv2-SMI", "name" : "Counter64"}, {"module" : "SNMPv2-SMI", "name" : "Integer32"}, {"module" : "SNMPv2-SMI", "name" : "TimeTicks"}, {"module" : "SNMPv2-SMI", "name" : "NOTIFICATION-TYPE"}, {"module" : "SNMPv2-TC", "name" : "TEXTUAL-CONVENTION"}, {"module" : "SNMPv2-TC", "name" : "DisplayString"}, {"module" : "SNMPv2-TC", "name" : "TruthValue"}, {"module" : "SNMPv2-TC", "name" : "TimeStamp"}, {"module" : "SNMPv2-MIB", "name" : "snmpTraps"}, ), "nodes" : { "lightcom" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728", }, # node "netPing" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.50", "status" : "current", }, # node "npReboot" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.911", }, # node "npSoftReboot" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.911.1", "status" : "current", "syntax" : { "type" : { "module" :"", "name" : "Integer32"}, }, "access" : "readwrite", "description" : """Write 1 to reboot device after current operations completition""", }, # scalar "npResetStack" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.911.2", "status" : "current", "syntax" : { "type" : { "module" :"", "name" : "Integer32"}, }, "access" : "readwrite", "description" : """Write 1 to re-initialize network stack""", }, # scalar "npForcedReboot" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.911.3", "status" : "current", "syntax" : { "type" : { "module" :"", "name" : "Integer32"}, }, "access" : "readwrite", "description" : """Write 1 to immediate forced reboot""", }, # scalar "npRelay" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.5500", }, # node "npRelayTable" : { "nodetype" : "table", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.5500.5", "status" : "current", "description" : """Watchdog and outlet/relay control table""", }, # table "npRelayEntry" : { "nodetype" : "row", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.5500.5.1", "status" : "current", "linkage" : [ "npRelayN", ], "description" : """Relay/outlet table row""", }, # row "npRelayN" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.5500.5.1.1", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "1", "max" : "2" }, ], "range" : { "min" : "1", "max" : "2" }, }, }, "access" : "readonly", "description" : """The N of output relay""", }, # column "npRelayMode" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.5500.5.1.2", "status" : "current", "syntax" : { "type" : { "basetype" : "Enumeration", "off" : { "nodetype" : "namednumber", "number" : "0" }, "on" : { "nodetype" : "namednumber", "number" : "1" }, "watchdog" : { "nodetype" : "namednumber", "number" : "2" }, "schedule" : { "nodetype" : "namednumber", "number" : "3" }, "scheduleAndWatchdog" : { "nodetype" : "namednumber", "number" : "4" }, "logic" : { "nodetype" : "namednumber", "number" : "5" }, }, }, "access" : "readwrite", "description" : """Control of relay: 0 - manual off 1 - manual on 2 - watchdog 3 - schedule 4 - both schedule and watchdog (while switched on by schedule) 5 - logic""", }, # column "npRelayStartReset" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.5500.5.1.3", "status" : "current", "syntax" : { "type" : { "module" :"", "name" : "Integer32"}, }, "access" : "readwrite", "description" : """Write 1 to start reset (switch relay off for some time)""", }, # column "npRelayMemo" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.5500.5.1.6", "status" : "current", "syntax" : { "type" : { "module" :"SNMPv2-TC", "name" : "DisplayString"}, }, "access" : "readonly", "description" : """Relay memo""", }, # column "npRelayFlip" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.5500.5.1.14", "status" : "current", "syntax" : { "type" : { "basetype" : "Enumeration", "flip" : { "nodetype" : "namednumber", "number" : "-1" }, }, }, "access" : "readwrite", "description" : """Write -1 to flip between manual on and manual off states of relay""", }, # column "npRelayState" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.5500.5.1.15", "status" : "current", "syntax" : { "type" : { "basetype" : "Enumeration", "off" : { "nodetype" : "namednumber", "number" : "0" }, "on" : { "nodetype" : "namednumber", "number" : "1" }, }, }, "access" : "readonly", "description" : """Actual relay state at the moment, regardless of source of control. 0 - relay is off 1 - relay is on""", }, # column "npPwr" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.5800", }, # node "npPwrTable" : { "nodetype" : "table", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.5800.3", "status" : "current", "description" : """Watchdog and outlet/relay control table""", }, # table "npPwrEntry" : { "nodetype" : "row", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.5800.3.1", "status" : "current", "linkage" : [ "npPwrChannelN", ], "description" : """Watchdog and outlet/relay control table row""", }, # row "npPwrChannelN" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.5800.3.1.1", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "1", "max" : "2" }, ], "range" : { "min" : "1", "max" : "2" }, }, }, "access" : "readonly", "description" : """The id of watchdog/power channel""", }, # column "npPwrStartReset" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.5800.3.1.2", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "0", "max" : "2" }, ], "range" : { "min" : "0", "max" : "2" }, }, }, "access" : "readwrite", "description" : """Write 1 to start forced reset On read: 0 - normal operation 1 - reset is active 2 - reboot pause is active or watchdog is inactive""", }, # column "npPwrManualMode" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.5800.3.1.3", "status" : "current", "syntax" : { "type" : { "basetype" : "Enumeration", "off" : { "nodetype" : "namednumber", "number" : "0" }, "on" : { "nodetype" : "namednumber", "number" : "1" }, "watchdog" : { "nodetype" : "namednumber", "number" : "2" }, "schedule" : { "nodetype" : "namednumber", "number" : "3" }, "scheduleAndWatchdog" : { "nodetype" : "namednumber", "number" : "4" }, "logic" : { "nodetype" : "namednumber", "number" : "5" }, }, }, "access" : "readwrite", "description" : """Mode of power channel 0 - manual off 1 - manual on 2 - watchdog 3 - schedule 4 - schedule and watchdog 5 - controlled by Logic""", }, # column "npPwrResetsCounter" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.5800.3.1.4", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "0", "max" : "65535" }, ], "range" : { "min" : "0", "max" : "65535" }, }, }, "access" : "readwrite", "description" : """Counter of watchdog resets Write 0 to clear.""", }, # column "npPwrRepeatingResetsCounter" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.5800.3.1.5", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "0", "max" : "65535" }, ], "range" : { "min" : "0", "max" : "65535" }, }, }, "access" : "readonly", "description" : """Counter of continous failed watchdog resets""", }, # column "npPwrMemo" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.5800.3.1.6", "status" : "current", "syntax" : { "type" : { "module" :"SNMPv2-TC", "name" : "DisplayString"}, }, "access" : "readonly", "description" : """Relay channel memo""", }, # column "npPwrRelayFlip" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.5800.3.1.14", "status" : "current", "syntax" : { "type" : { "basetype" : "Enumeration", "flip" : { "nodetype" : "namednumber", "number" : "-1" }, }, }, "access" : "readwrite", "description" : """Write -1 to flip between manual on and manual off states of relay""", }, # column "npPwrRelayState" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.5800.3.1.15", "status" : "current", "syntax" : { "type" : { "basetype" : "Enumeration", "off" : { "nodetype" : "namednumber", "number" : "0" }, "on" : { "nodetype" : "namednumber", "number" : "1" }, }, }, "access" : "readonly", "description" : """Actual relay state at the moment, regardless of source of control. 0 - relay is off 1 - relay is on""", }, # column "npIr" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.7900", }, # node "npIrCtrl" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.7900.1", }, # node "npIrPlayCmd" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.7900.1.1", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "1", "max" : "16" }, ], "range" : { "min" : "1", "max" : "16" }, }, }, "access" : "readwrite", "description" : """Write IR command number to send IR command""", }, # scalar "npIrReset" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.7900.1.2", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "0", "max" : "1" }, ], "range" : { "min" : "0", "max" : "1" }, }, }, "access" : "readwrite", "description" : """Write 1 to reset IR transiever. After reset, send IR command and check npIrStatus.""", }, # scalar "npIrStatus" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.7900.1.3", "status" : "current", "syntax" : { "type" : { "basetype" : "Enumeration", "commandCompleted" : { "nodetype" : "namednumber", "number" : "0" }, "protocolError" : { "nodetype" : "namednumber", "number" : "1" }, "commandAccepted" : { "nodetype" : "namednumber", "number" : "2" }, "errorUnknown" : { "nodetype" : "namednumber", "number" : "16" }, "errorBadNumber" : { "nodetype" : "namednumber", "number" : "17" }, "errorEmptyRecord" : { "nodetype" : "namednumber", "number" : "18" }, "errorFlashChip" : { "nodetype" : "namednumber", "number" : "19" }, "errorTimeout" : { "nodetype" : "namednumber", "number" : "20" }, "errorExtBusBusy" : { "nodetype" : "namednumber", "number" : "21" }, }, }, "access" : "readonly", "description" : """IR transiever status""", }, # scalar "npCurLoop" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8300", }, # node "npCurLoopTable" : { "nodetype" : "table", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8300.1", "status" : "current", "description" : """Current loop sensors Table""", }, # table "npCurLoopEntry" : { "nodetype" : "row", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8300.1.1", "status" : "current", "linkage" : [ "npCurLoopN", ], "description" : """Current loop sensors Table Row""", }, # row "npCurLoopN" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8300.1.1.1", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "1", "max" : "8" }, ], "range" : { "min" : "1", "max" : "8" }, }, }, "access" : "readonly", "description" : """Index of current loop, 1 to max supported""", }, # column "npCurLoopStatus" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8300.1.1.2", "status" : "current", "syntax" : { "type" : { "basetype" : "Enumeration", "ok" : { "nodetype" : "namednumber", "number" : "0" }, "alert" : { "nodetype" : "namednumber", "number" : "1" }, "cut" : { "nodetype" : "namednumber", "number" : "2" }, "short" : { "nodetype" : "namednumber", "number" : "3" }, "notPowered" : { "nodetype" : "namednumber", "number" : "4" }, }, }, "access" : "readonly", "description" : """Status of the loop 0=Normal, 1=Alert, 2=Cut, 3=Short, 4=Not Powered""", }, # column "npCurLoopI" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8300.1.1.3", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "0", "max" : "99999" }, ], "range" : { "min" : "0", "max" : "99999" }, }, }, "access" : "readonly", "description" : """Loop current, mA""", }, # column "npCurLoopV" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8300.1.1.4", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "0", "max" : "99999" }, ], "range" : { "min" : "0", "max" : "99999" }, }, }, "access" : "readonly", "description" : """Voltage drop on the loop, mV""", }, # column "npCurLoopR" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8300.1.1.5", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "0", "max" : "99999" }, ], "range" : { "min" : "0", "max" : "99999" }, }, }, "access" : "readonly", "description" : """Resistance of the loop, Ohm""", }, # column "npCurLoopPower" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8300.1.1.7", "status" : "current", "syntax" : { "type" : { "basetype" : "Enumeration", "off" : { "nodetype" : "namednumber", "number" : "0" }, "on" : { "nodetype" : "namednumber", "number" : "1" }, "cyclePower" : { "nodetype" : "namednumber", "number" : "2" }, }, }, "access" : "readwrite", "description" : """Control of loop power 0=Off, 1=On, 2=reset by off/on power""", }, # column "npCurLoopTraps" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8300.2", }, # node "npCurLoopTrapPrefix" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8300.2.0", }, # node "npCurLoopTrapN" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8300.2.1", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "1", "max" : "8" }, ], "range" : { "min" : "1", "max" : "8" }, }, }, "access" : "readonly", "description" : """Index of current loop, which status has changed""", }, # scalar "npCurLoopTrapStatus" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8300.2.2", "status" : "current", "syntax" : { "type" : { "basetype" : "Enumeration", "ok" : { "nodetype" : "namednumber", "number" : "0" }, "alert" : { "nodetype" : "namednumber", "number" : "1" }, "cut" : { "nodetype" : "namednumber", "number" : "2" }, "short" : { "nodetype" : "namednumber", "number" : "3" }, "notPowered" : { "nodetype" : "namednumber", "number" : "4" }, }, }, "access" : "readonly", "description" : """Status of the loop 0=Normal, 1=Alert, 2=Cut, 3=Short, 4=Not Powered""", }, # scalar "npCurLoopTrapI" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8300.2.3", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "0", "max" : "99999" }, ], "range" : { "min" : "0", "max" : "99999" }, }, }, "access" : "readonly", "description" : """Loop current, mA""", }, # scalar "npCurLoopTrapV" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8300.2.4", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "0", "max" : "99999" }, ], "range" : { "min" : "0", "max" : "99999" }, }, }, "access" : "readonly", "description" : """Voltage drop on the loop, mV""", }, # scalar "npCurLoopTrapR" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8300.2.5", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "0", "max" : "99999" }, ], "range" : { "min" : "0", "max" : "99999" }, }, }, "access" : "readonly", "description" : """Resistance of the loop, Ohm""", }, # scalar "npCurLoopTrapPower" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8300.2.7", "status" : "current", "syntax" : { "type" : { "basetype" : "Enumeration", "off" : { "nodetype" : "namednumber", "number" : "0" }, "on" : { "nodetype" : "namednumber", "number" : "1" }, }, }, "access" : "readwrite", "description" : """Status of loop power 0=Off, 1=On""", }, # scalar "npRelHumidity" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8400", }, # node "npRelHumSensor" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8400.2", }, # node "npRelHumSensorValueH" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8400.2.2", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "0", "max" : "100" }, ], "range" : { "min" : "0", "max" : "100" }, }, }, "access" : "readonly", "description" : """Relative humidity value, %""", }, # scalar "npRelHumSensorStatus" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8400.2.3", "status" : "current", "syntax" : { "type" : { "basetype" : "Enumeration", "error" : { "nodetype" : "namednumber", "number" : "0" }, "ok" : { "nodetype" : "namednumber", "number" : "1" }, }, }, "access" : "readonly", "description" : """Status of the Rel.Humidity Sensor One 0=Normal, 1=Error or Sensor isn't connected""", }, # scalar "npRelHumSensorValueT" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8400.2.4", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "-60", "max" : "200" }, ], "range" : { "min" : "-60", "max" : "200" }, }, }, "access" : "readonly", "description" : """Sensor temperature, deg.C""", }, # scalar "npRelHumSensorStatusH" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8400.2.5", "status" : "current", "syntax" : { "type" : { "basetype" : "Enumeration", "sensorFailed" : { "nodetype" : "namednumber", "number" : "0" }, "belowSafeRange" : { "nodetype" : "namednumber", "number" : "1" }, "inSafeRange" : { "nodetype" : "namednumber", "number" : "2" }, "aboveSafeRange" : { "nodetype" : "namednumber", "number" : "3" }, }, }, "access" : "readonly", "description" : """Status of Relative Humiduty""", }, # scalar "npRelHumSafeRangeHigh" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8400.2.7", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "0", "max" : "100" }, ], "range" : { "min" : "0", "max" : "100" }, }, }, "access" : "readonly", "description" : """Relative Humidity safe range, top margin, %RH""", }, # scalar "npRelHumSafeRangeLow" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8400.2.8", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "0", "max" : "100" }, ], "range" : { "min" : "0", "max" : "100" }, }, }, "access" : "readonly", "description" : """Relative Humidity safe range, bottom margin, %RH""", }, # scalar "npRelHumSensorValueT100" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8400.2.9", "status" : "current", "syntax" : { "type" : { "module" :"", "name" : "Integer32"}, }, "access" : "readonly", "description" : """Sensor temperature, deg.C * 100 (fixed point two decimal places) Used to get access to the fractional part of T value""", }, # scalar "npRelHumTraps" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8400.9", }, # node "npRelHumTrapPrefix" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8400.9.0", }, # node "npThermo" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8800", }, # node "npThermoTable" : { "nodetype" : "table", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8800.1", "status" : "current", "description" : """Thermo Sensors Table""", }, # table "npThermoEntry" : { "nodetype" : "row", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8800.1.1", "status" : "current", "linkage" : [ "npThermoSensorN", ], "description" : """Thermo Sensors Table Row""", }, # row "npThermoSensorN" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8800.1.1.1", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "1", "max" : "8" }, ], "range" : { "min" : "1", "max" : "8" }, }, }, "access" : "readonly", "description" : """The id of temperature sensor, 1 to 8""", }, # column "npThermoValue" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8800.1.1.2", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "-60", "max" : "280" }, ], "range" : { "min" : "-60", "max" : "280" }, }, }, "access" : "readonly", "description" : """Temperature, deg.C""", }, # column "npThermoStatus" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8800.1.1.3", "status" : "current", "syntax" : { "type" : { "basetype" : "Enumeration", "failed" : { "nodetype" : "namednumber", "number" : "0" }, "low" : { "nodetype" : "namednumber", "number" : "1" }, "norm" : { "nodetype" : "namednumber", "number" : "2" }, "high" : { "nodetype" : "namednumber", "number" : "3" }, }, }, "access" : "readonly", "description" : """Temperature status (0=fault, 1=underheat, 2=normal, 3=overheat)""", }, # column "npThermoLow" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8800.1.1.4", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "-60", "max" : "280" }, ], "range" : { "min" : "-60", "max" : "280" }, }, }, "access" : "readonly", "description" : """Bottom margin of normal temperature range, deg.C""", }, # column "npThermoHigh" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8800.1.1.5", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "-60", "max" : "280" }, ], "range" : { "min" : "-60", "max" : "280" }, }, }, "access" : "readonly", "description" : """Top margin of normal temperature range, deg.C""", }, # column "npThermoMemo" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8800.1.1.6", "status" : "current", "syntax" : { "type" : { "module" :"SNMPv2-TC", "name" : "DisplayString"}, }, "access" : "readonly", "description" : """T channel memo""", }, # column "npThermoTraps" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8800.2", }, # node "npThermoTrapPrefix" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8800.2.0", }, # node "npThermoTrapSensorN" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8800.2.1", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "1", "max" : "8" }, ], "range" : { "min" : "1", "max" : "8" }, }, }, "access" : "readonly", "description" : """The id of temperature sensor, 1 to 8""", }, # scalar "npThermoTrapValue" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8800.2.2", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "-60", "max" : "280" }, ], "range" : { "min" : "-60", "max" : "280" }, }, }, "access" : "readonly", "description" : """Temperature, deg.C""", }, # scalar "npThermoTrapStatus" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8800.2.3", "status" : "current", "syntax" : { "type" : { "basetype" : "Enumeration", "failed" : { "nodetype" : "namednumber", "number" : "0" }, "low" : { "nodetype" : "namednumber", "number" : "1" }, "norm" : { "nodetype" : "namednumber", "number" : "2" }, "high" : { "nodetype" : "namednumber", "number" : "3" }, }, }, "access" : "readonly", "description" : """Temperature status (0=fault, 1=underheat, 2=normal, 3=overheat)""", }, # scalar "npThermoTrapLow" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8800.2.4", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "-60", "max" : "280" }, ], "range" : { "min" : "-60", "max" : "280" }, }, }, "access" : "readonly", "description" : """Bottom margin of normal temperature range, deg.C""", }, # scalar "npThermoTrapHigh" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8800.2.5", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "-60", "max" : "280" }, ], "range" : { "min" : "-60", "max" : "280" }, }, }, "access" : "readonly", "description" : """Top margin of normal temperature range, deg.C""", }, # scalar "npThermoTrapMemo" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8800.2.6", "status" : "current", "syntax" : { "type" : { "module" :"SNMPv2-TC", "name" : "DisplayString"}, }, "access" : "readonly", "description" : """T channel memo""", }, # scalar "npIo" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8900", }, # node "npIoTable" : { "nodetype" : "table", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8900.1", "status" : "current", "description" : """Digital Input/output Table""", }, # table "npIoEntry" : { "nodetype" : "row", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8900.1.1", "status" : "current", "linkage" : [ "npIoLineN", ], "description" : """Digital Input/output Table Row""", }, # row "npIoLineN" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8900.1.1.1", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "1", "max" : "16" }, ], "range" : { "min" : "1", "max" : "16" }, }, }, "access" : "readonly", "description" : """Number of IO line, from 1 to max supported""", }, # column "npIoLevelIn" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8900.1.1.2", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "0", "max" : "1" }, ], "range" : { "min" : "0", "max" : "1" }, }, }, "access" : "readonly", "description" : """Input level, 0 or 1""", }, # column "npIoLevelOut" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8900.1.1.3", "status" : "current", "syntax" : { "type" : { "module" :"", "name" : "Integer32"}, }, "access" : "readwrite", "description" : """Output level, 0 or 1. Write -1 to flip output.""", }, # column "npIoMemo" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8900.1.1.6", "status" : "current", "syntax" : { "type" : { "module" :"SNMPv2-TC", "name" : "DisplayString"}, }, "access" : "readonly", "description" : """IO line memo""", }, # column "npIoPulseCounter" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8900.1.1.9", "status" : "current", "syntax" : { "type" : { "module" :"SNMPv2-SMI", "name" : "Counter32"}, }, "access" : "readwrite", "description" : """Pulse Counter on IO input line (counts positive fronts) Write 0 to reset.""", }, # column "npIoSinglePulseDuration" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8900.1.1.12", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "100", "max" : "25500" }, ], "range" : { "min" : "100", "max" : "25500" }, }, }, "access" : "readwrite", "description" : """Set duration of single pulse on IO output line, 100ms to 25500ms, min. step is 100ms""", }, # column "npIoSinglePulseStart" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8900.1.1.13", "status" : "current", "syntax" : { "type" : { "module" :"", "name" : "Integer32"}, }, "access" : "readwrite", "description" : """Write 1 to start single pulse on IO output. Output will be inverted for time, specified by npIoSinglePulseDuration""", }, # column "npIoTraps" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8900.2", }, # node "npIoTrapPrefix" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8900.2.0", }, # node "npIoTrapLineN" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8900.2.1", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "1", "max" : "16" }, ], "range" : { "min" : "1", "max" : "16" }, }, }, "access" : "readonly", "description" : """Trap data, Number of IO line""", }, # scalar "npIoTrapLevelIn" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8900.2.2", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "0", "max" : "1" }, ], "range" : { "min" : "0", "max" : "1" }, }, }, "access" : "readonly", "description" : """Trap data, new Input level, 0 or 1""", }, # scalar "npIoTrapMemo" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8900.2.6", "status" : "current", "syntax" : { "type" : { "module" :"SNMPv2-TC", "name" : "DisplayString"}, }, "access" : "readonly", "description" : """Trap data, IO line memo""", }, # scalar "npIoTrapLevelLegend" : { "nodetype" : "scalar", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8900.2.7", "status" : "current", "syntax" : { "type" : { "module" :"SNMPv2-TC", "name" : "DisplayString"}, }, "access" : "readonly", "description" : """Legend for current logic level on the IO line""", }, # scalar "npElecMeter" : { "nodetype" : "node", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.9700", }, # node "npElecTable" : { "nodetype" : "table", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.9700.1", "status" : "current", "description" : """Electricity Meter Table""", }, # table "npElecEntry" : { "nodetype" : "row", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.9700.1.1", "status" : "current", "linkage" : [ "npElecIndex", ], "description" : """Electricity Meter Table Table Row""", }, # row "npElecIndex" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.9700.1.1.1", "status" : "current", "syntax" : { "type" : { "basetype" : "Integer32", "ranges" : [ { "min" : "1", "max" : "4" }, ], "range" : { "min" : "1", "max" : "4" }, }, }, "access" : "readonly", "description" : """Number of elec.meter, associated with IO line""", }, # column "npElecPulsesPerKwh" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.9700.1.1.2", "status" : "current", "syntax" : { "type" : { "module" :"", "name" : "Integer32"}, }, "access" : "readwrite", "description" : """Pulses on IO line input per 1 kWh of consumed energy""", }, # column "npElecPower" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.9700.1.1.3", "status" : "current", "syntax" : { "type" : { "module" :"SNMPv2-SMI", "name" : "Gauge32"}, }, "access" : "readonly", "description" : """Power, Watts, based on pulse rate/interval, 5 minute average""", }, # column "npElecEnergy" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.9700.1.1.4", "status" : "current", "syntax" : { "type" : { "module" :"SNMPv2-SMI", "name" : "Counter32"}, }, "access" : "readwrite", "description" : """Energy counter, kWh, based on pulse count""", }, # column "npElecEnergy100" : { "nodetype" : "column", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.9700.1.1.5", "status" : "current", "syntax" : { "type" : { "module" :"SNMPv2-SMI", "name" : "Counter32"}, }, "access" : "readwrite", "description" : """Energy counter, kWh*100, based on pulse count""", }, # column }, # nodes "notifications" : { "npCurLoopTrap" : { "nodetype" : "notification", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8300.2.0.1", "status" : "current", "objects" : { "npCurLoopTrapN" : { "nodetype" : "object", "module" : "DKSF-60-4-X-X-X" }, "npCurLoopTrapStatus" : { "nodetype" : "object", "module" : "DKSF-60-4-X-X-X" }, "npCurLoopTrapI" : { "nodetype" : "object", "module" : "DKSF-60-4-X-X-X" }, "npCurLoopTrapV" : { "nodetype" : "object", "module" : "DKSF-60-4-X-X-X" }, "npCurLoopTrapR" : { "nodetype" : "object", "module" : "DKSF-60-4-X-X-X" }, "npCurLoopTrapPower" : { "nodetype" : "object", "module" : "DKSF-60-4-X-X-X" }, }, "description" : """Status of current loop N has changed!""", }, # notification "npRelHumTrap" : { "nodetype" : "notification", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8400.9.0.1", "status" : "current", "objects" : { "npRelHumSensorStatusH" : { "nodetype" : "object", "module" : "DKSF-60-4-X-X-X" }, "npRelHumSensorValueH" : { "nodetype" : "object", "module" : "DKSF-60-4-X-X-X" }, "npRelHumSafeRangeHigh" : { "nodetype" : "object", "module" : "DKSF-60-4-X-X-X" }, "npRelHumSafeRangeLow" : { "nodetype" : "object", "module" : "DKSF-60-4-X-X-X" }, }, "description" : """Status of Relative Humidity RH sensor has changed!""", }, # notification "npThermoTrap" : { "nodetype" : "notification", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8800.2.0.1", "status" : "current", "objects" : { "npThermoTrapSensorN" : { "nodetype" : "object", "module" : "DKSF-60-4-X-X-X" }, "npThermoTrapValue" : { "nodetype" : "object", "module" : "DKSF-60-4-X-X-X" }, "npThermoTrapStatus" : { "nodetype" : "object", "module" : "DKSF-60-4-X-X-X" }, "npThermoTrapLow" : { "nodetype" : "object", "module" : "DKSF-60-4-X-X-X" }, "npThermoTrapHigh" : { "nodetype" : "object", "module" : "DKSF-60-4-X-X-X" }, "npThermoTrapMemo" : { "nodetype" : "object", "module" : "DKSF-60-4-X-X-X" }, }, "description" : """Status of Thermo sensor is changed (crossing of normal temp. range)""", }, # notification "npIoTrap" : { "nodetype" : "notification", "moduleName" : "DKSF-60-4-X-X-X", "oid" : "1.3.6.1.4.1.25728.8900.2.0.1", "status" : "current", "objects" : { "npIoTrapLineN" : { "nodetype" : "object", "module" : "DKSF-60-4-X-X-X" }, "npIoTrapLevelIn" : { "nodetype" : "object", "module" : "DKSF-60-4-X-X-X" }, "npIoTrapMemo" : { "nodetype" : "object", "module" : "DKSF-60-4-X-X-X" }, "npIoTrapLevelLegend" : { "nodetype" : "object", "module" : "DKSF-60-4-X-X-X" }, }, "description" : """Input state of IO line is changed""", }, # notification }, # notifications }
#!/usr/bin/python # # Copyright 2018 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Author: <NAME> # Enhanced by <NAME> # Enhanced by <NAME> # This is the main class for the project. # createReportObject # createBigQueryService # returnUsersListGenerator # returnCustomerUsageReport # returnUserUsageReport # returnAuditLogReport # writeDatainBigQuery # stream_row_to_bigquery # Implements config.yaml to store all the variables of the project # Google APIs, BQ and GAE Requirements import logging import webapp2 import time from httplib2 import Http from google.appengine.api.urlfetch_errors import DeadlineExceededError from oauth2client.service_account import ServiceAccountCredentials from googleapiclient.discovery import build import hashlib from datetime import date, timedelta from google.appengine.api import urlfetch from bvi_logger import bvi_log import yaml with open("config.yaml", 'r') as ymlfile: cfg = yaml.load(ymlfile) today = date.today() dDate = today.strftime("%Y-%m-%d") maxResultsPage = cfg['task_management']['page_size'] if cfg['plan'] == 'Business': maxResultsPage_UserUsage = cfg['task_management']['page_size_user_usage'] # Added to avoid DeadlineExceededError: Deadline exceeded while waiting for HTTP response from URL: urlfetch.set_default_fetch_deadline(60) # Create Report Object # Parameters: # sScope = Service Scope to Authorise # report1 = Type of report API object 1 # report2 = Type of report API object 2 # SAJson = Service Account Json Authentication File # SADelegated = SuperAdmin Delegated Account # # Creates a Reports API object with a JSON file and a defined scope. # Needs to delegate the credentials, as the service account in the JSON file # has no credentials to query Google Apps APIs and a SA (SuperAdmin) has. # # Returns: Reports API Object to query def refresh_credentials(credentials, num_retries=5, timeout=5): retried = 0 while retried < num_retries: try: retried += 1 http_auth = credentials.authorize(Http(timeout=120)) break except Exception as err: if retried == num_retries: raise err logging.info("Retrying {} of {} token refresh!".format(str(retried), str(num_retries))) time.sleep(timeout) return http_auth def execute_request_with_retries(request, num_retries=5, timeout=5): retried = 0 while retried < num_retries: try: retried += 1 result = request.execute() break except Exception as err: if retried == num_retries: raise err logging.info("Retrying {} of {} request!".format(str(retried), str(num_retries))) time.sleep(timeout) return result def createReportObject(sScope, report1, report2, SAJson, SADelegated): logging.info(sScope) logging.info(report1) logging.info(report2) logging.info(SAJson) try: logging.info("Impersonating {SA}".format(SA=SADelegated)) credentials = ServiceAccountCredentials.from_json_keyfile_name(SAJson, sScope) logging.info(credentials) delegated_credentials = credentials.create_delegated(SADelegated) logging.info(delegated_credentials) http_auth_delegated = refresh_credentials(delegated_credentials) logging.info(http_auth_delegated) return build(report1, report2, http=http_auth_delegated) except Exception as err: logging.error(err) raise err # Create BigQuery Object # Parameters: # sScope = Service Scope to Authorise # report1 = Type of report API object 1 # report2 = Type of report API object 2 # # Creates a BigQuery object with a JSON file and a defined scope. # # Returns: BigQuery Object to query def createBigQueryService(sScope, report1, report2): credentials = ServiceAccountCredentials.from_json_keyfile_name(cfg['credentials']['bigquery'], sScope) http_auth = refresh_credentials(credentials) bigquery_service = build(report1, report2, credentials=credentials, http=http_auth) return bigquery_service # Review and delete ? # Returns User List # Parameters: # dDomain = Google Apps Domain # SAJson = Service Account Json Authentication File # SADelegated = SuperAdmin Delegated Account # # Generates a list of users in a given GAps domain # # Returns: JSON list of users, for a give date, with the defined fields def returnUsersListGenerator(dDomain, SAJson, SADelegated): users = [] page_token = None reports = createReportObject(cfg['scopes']['admin_directory'], 'admin', 'directory_v1', SAJson, SADelegated) # AVAILABLE FIELDS: https://developers.google.com/admin-sdk/directory/v1/reference/users#resource fields = 'nextPageToken,users(creationTime,customerId,emails,lastLoginTime,orgUnitPath,primaryEmail)' while True: try: request = reports.users().list(domain=dDomain, pageToken=page_token, fields=fields) results = execute_request_with_retries(request) if 'users' in results: users = results['users'] logging.info("Page Token {}".format(page_token)) for user_item in users: user_item[u'date'] = dDate if 'nextPageToken' in results: page_token = results['nextPageToken'] logging.info("We have {} user rows, and more to come".format(len(users))) yield users else: break except DeadlineExceededError as err: logging.error(err) logging.error("Retrying!") except Exception as err: logging.error(err) logging.error("Error Found, ending returnUsersListGenerator!") break logging.info("We have {} user rows in the end".format(len(users))) yield users def returnUsersList(dDomain, SAJson, SADelegated): all_users = [] for users in returnUsersListGenerator(dDomain, SAJson, SADelegated): all_users += users return all_users def returnTotalUsersList(dDomain, SAJson, SADelegated): all_users = [] for users in returnUsersListGenerator(dDomain, SAJson, SADelegated): all_users += users return len(all_users) # Returns Customer Usage Report # Parameters: # dDay = A given date # SAJson = Service Account Json Authentication File # SADelegated = SuperAdmin Delegated Account # # Generates a Customer Usage Report for a given day -5 (Due to data availability) # # Returns: JSON with the Customer Usage Metrics def returnCustomerUsageReport(dDay, SAJson, SADelegated): usage = [] page_token = None try: reports = createReportObject(cfg['scopes']['admin_report'], 'admin', 'reports_v1', SAJson, SADelegated) except Exception as err: bvi_log(date=dDay, resource='customer_usage', message_id='invalid_credential', message=err, regenerate=True) raise err fields = 'nextPageToken,usageReports(date,entity,parameters)' parameters = 'accounts:gsuite_enterprise_used_licenses,accounts:gsuite_enterprise_total_licenses,accounts:gsuite_unlimited_used_licenses,accounts:gsuite_unlimited_total_licenses,accounts:gsuite_basic_used_licenses,accounts:gsuite_basic_total_licenses,accounts:num_disabled_accounts,accounts:num_suspended_users,accounts:num_users,accounts:apps_total_licenses,accounts:apps_used_licenses,accounts:vault_total_licenses,accounts:gsuite_enterprise_used_licenses,accounts:gsuite_basic_used_licenses,accounts:gsuite_unlimited_used_licenses,accounts:gsuite_enterprise_total_licenses,accounts:gsuite_unlimited_total_licenses,accounts:gsuite_basic_total_licenses,accounts:num_1day_logins,accounts:num_7day_logins,accounts:num_30day_logins,' \ 'calendar:num_1day_active_users,calendar:num_7day_active_users,calendar:num_30day_active_users,' \ 'cros:num_7day_active_devices,cros:num_30day_active_devices,' \ 'classroom:num_1day_users,classroom:num_7day_users,classroom:num_30day_users,' \ 'drive:num_1day_active_users,drive:num_7day_active_users,drive:num_30day_active_users,' \ 'docs:num_docs,drive:num_1day_active_users,drive:num_7day_active_users,drive:num_30day_active_users,drive:num_30day_google_documents_active_users,drive:num_30day_google_spreadsheets_active_users,drive:num_30day_google_presentations_active_users,drive:num_30day_google_forms_active_users,drive:num_30day_google_drawings_active_users,drive:num_30day_other_types_active_users,drive:num_creators,drive:num_collaborators,drive:num_consumers,drive:num_sharers,' \ 'gmail:num_1day_active_users,gmail:num_7day_active_users,gmail:num_30day_active_users,' \ 'gplus:num_1day_active_users,gplus:num_7day_active_users,gplus:num_30day_active_users,' \ 'device_management:num_30day_google_sync_managed_devices,device_management:num_30day_android_managed_devices,device_management:num_30day_ios_managed_devices,device_management:num_30day_total_managed_devices,device_management:num_30day_google_sync_managed_users,device_management:num_30day_android_managed_users,device_management:num_30day_ios_managed_users,device_management:num_30day_total_managed_users,' \ 'sites:num_sites,sites:num_sites_created,' \ 'meet:average_meeting_minutes,meet:average_meeting_minutes_with_11_to_15_calls,meet:average_meeting_minutes_with_16_to_25_calls,meet:average_meeting_minutes_with_26_to_50_calls,meet:average_meeting_minutes_with_2_calls,meet:average_meeting_minutes_with_3_to_5_calls,meet:average_meeting_minutes_with_6_to_10_calls,meet:lonely_meetings,meet:max_concurrent_usage_chromebase,meet:max_concurrent_usage_chromebox,meet:num_1day_active_users,meet:num_30day_active_users,meet:num_7day_active_users,' \ 'meet:num_calls,meet:num_calls_android,meet:num_calls_by_external_users,meet:num_calls_by_internal_users,meet:num_calls_by_pstn_in_users,meet:num_calls_chromebase,meet:num_calls_chromebox,meet:num_calls_ios,meet:num_calls_jamboard,meet:num_calls_unknown_client,meet:num_calls_web,meet:num_meetings,meet:num_meetings_android,meet:num_meetings_chromebase,meet:num_meetings_chromebox,meet:num_meetings_ios,meet:num_meetings_jamboard,meet:num_meetings_unknown_client,meet:num_meetings_web,' \ 'meet:num_meetings_with_11_to_15_calls,meet:num_meetings_with_16_to_25_calls,meet:num_meetings_with_26_to_50_calls,meet:num_meetings_with_2_calls,meet:num_meetings_with_3_to_5_calls,meet:num_meetings_with_6_to_10_calls,meet:num_meetings_with_external_users,meet:num_meetings_with_pstn_in_users,meet:total_call_minutes,meet:total_call_minutes_android,meet:total_call_minutes_by_external_users,meet:total_call_minutes_by_internal_users,meet:total_call_minutes_by_pstn_in_users,' \ 'meet:total_call_minutes_chromebase,meet:total_call_minutes_chromebox,meet:total_call_minutes_ios,meet:total_call_minutes_jamboard,meet:total_call_minutes_unknown_client,meet:total_call_minutes_web,meet:total_meeting_minutes,' \ 'gplus:num_1day_active_users,gplus:num_7day_active_users,gplus:num_30day_active_users,gplus:num_shares,gplus:num_stream_items_read,gplus:num_plusones,gplus:num_replies,gplus:num_reshares,gplus:num_communities,gplus:num_communities_public,gplus:num_communities_private,gplus:num_communities_organization_wide,gplus:num_communities_organization_private' while True: try: request = reports.customerUsageReports().get(date=dDay, fields=fields, pageToken=page_token, parameters=parameters) results = execute_request_with_retries(request) usage = results.get('usageReports', []) if 'nextPageToken' in results: page_token = results['nextPageToken'] logging.info("We have {} customer_usage rows, and more to come".format(len(usage))) yield usage else: break except DeadlineExceededError as err: logging.error(err) logging.error("Retrying!") except Exception as err: logging.error(err) logging.error("Error Found, ending returnCustomerUsageReport!") bvi_log(date=dDay, resource='customer_usage', message_id='customer_usage_api', message=err, regenerate=True) break logging.info("We have {} customer_usage rows in the end".format(len(usage))) yield usage # Review and delete ? # Returns User Usage Report # Parameters: # uUser = User parameter or all for all users # dDay = A given date # SAJson = Service Account Json Authentication File # SADelegated = SuperAdmin Delegated Account # # Generates a User Usage Report for a given day -5 (Due to data availability) # # Returns: JSON with the User Usage Metrics def returnUserUsageReport(uUser, dDay, SAJson, SADelegated): user_usage = [] page_token = None reports = createReportObject(cfg['scopes']['admin_report'], 'admin', 'reports_v1', SAJson, SADelegated) fields = 'nextPageToken,usageReports(date,entity,parameters)' while True: try: request = reports.userUsageReport().get(userKey=uUser, date=dDay, fields=fields, pageToken=page_token) results = execute_request_with_retries(request) user_usage = results.get('usageReports', []) if 'nextPageToken' in results: page_token = results['nextPageToken'] logging.info("We have {} user_usage rows, and more to come".format(len(user_usage))) yield user_usage else: break except DeadlineExceededError as err: logging.error(err) logging.error("Retrying!") except Exception as err: logging.error(err) logging.error("Error Found, ending returnUserUsageReport !") break logging.info("We have {} user_usage rows in the end".format(len(user_usage))) yield user_usage # Review and delete ? # Returns Audit Log Report # Parameters: # uUser = User parameter or all for al users # appName = Defined app from the reports (admin, calendar, drive, groups, login, mobile, token) # dDay = A given date # SAJson = Service Account Json Authentication File # SADelegated = SuperAdmin Delegated Account # # Generates an Activities Report for a given day -5 (Due to data availability) # # Returns: JSON with the User Usage Metrics def returnAuditLogReport(uUser, appName, dDay, SAJson, SADelegated): startTime = dDay + 'T00:00:00.000Z' endTime = dDay + 'T23:59:59.999Z' page_token = None reports = createReportObject(cfg['scopes']['audit_log'], 'admin', 'reports_v1', SAJson, SADelegated) fields = 'items(actor,events,id),nextPageToken' while True: try: request = reports.activities().list(userKey=uUser, applicationName=appName, startTime=startTime, endTime=endTime, fields=fields, pageToken=page_token) results = execute_request_with_retries(request) activities = results.get('items', []) if 'nextPageToken' in results: page_token = results['nextPageToken'] logging.info("We have {} activities, and more to come".format(len(activities))) yield activities else: break except DeadlineExceededError as err: logging.error(err) logging.error("Retrying!") except Exception as err: logging.error(err) logging.error("Error Found, ending returnAuditLogReport!") break logging.info("We have {} activities in the end".format(len(activities))) yield activities # Writes Data in BigQuery # Parameters: # dataRows = rows of JSON data to be written in BigQuery # dataTable = table in BigQuery to write in # # Gets a JSON dataset and splits in rows to write in BigQuery # # Returns: BigQuery response def writeDatainBigQuery(dataRows, dataTable): bQService = createBigQueryService(cfg['scopes']['big_query'], 'bigquery', 'v2') logging.info("Processing {len} rows, destinated to {table}".format(len=len(dataRows), table=dataTable)) rows_list = [] responses = [] for index, row in enumerate(dataRows): rows_list.append(row) if (index + 1) % 500 == 0: responses.append(stream_row_to_bigquery(bQService, dataTable, rows_list)) rows_list = [] if len(dataRows) % 500 > 0: responses.append(stream_row_to_bigquery(bQService, dataTable, rows_list)) return responses def delete_table_big_query(table_name): bigquery = createBigQueryService(cfg['scopes']['big_query'], 'bigquery', 'v2') logging.info("Deleting {table}".format(table=table_name)) try: bigquery.tables().delete( projectId=cfg['ids']['project_id'], datasetId=cfg['ids']['dataset_id'], tableId=table_name ).execute() except Exception as err: logging.error(err) # Streams Row to BigQuery # Parameters: # bigquery = BigQuery service # row = a row of JSON data # # Streams one row of JSON data into BigQuery # # Returns: Nothing def stream_row_to_bigquery(bigquery, table_name, rows): logging.info("Streaming {len} rows, destinated to {table}".format(len=len(rows), table=table_name)) rows_list = [] for row in rows: rows_list.append({ 'json': row, # Generate a unique id for each row so retries don't accidentally # duplicate insert 'insertId': (hashlib.md5(str(row))).hexdigest(), }) insert_all_data = { 'skipInvalidRows': True, 'ignoreUnknownValues': True, 'rows': rows_list } try: retried = 0 while retried < 5: try: retried += 1 bqstream = bigquery.tabledata().insertAll( projectId=cfg['ids']['project_id'], datasetId=cfg['ids']['dataset_id'], tableId=table_name, body=insert_all_data).execute(num_retries=5) insertErrors = bqstream.get('insertErrors') break except Exception as err: if retried == 5: raise err logging.info("Retrying streaming!") time.sleep(5) if insertErrors: logging.error(insertErrors) except Exception as err: logging.error(err) bqstream = '' except: bqstream = '' return bqstream # Returns User List Extended # Parameters: # dDomain = Google Apps Domain # SAJson = Service Account Json Authentication File # SADelegated = SuperAdmin Delegated Account # # Generates a list of users in a given GAps domain # # Returns: JSON list of users, for a give date, with the defined fields def returnUsersListGeneratorExtended(dDomain, SAJson, SADelegated): users = [] page_token = None reports = createReportObject(cfg['scopes']['admin_directory'], 'admin', 'directory_v1', SAJson, SADelegated) # AVAILABLE FIELDS: https://developers.google.com/admin-sdk/directory/v1/reference/users#resource fields = 'nextPageToken,users' while True: try: request = reports.users().list(domain=dDomain, pageToken=page_token, maxResults=5, projection='full', fields=fields) results = execute_request_with_retries(request) if 'users' in results: users = results['users'] for user_item in users: user_item[u'date'] = dDate if 'nextPageToken' in results: page_token = results['nextPageToken'] logging.info("We have {} user rows, and more to come".format(len(users))) yield users else: break except DeadlineExceededError as err: logging.error(err) logging.error("Retrying!") except Exception as err: logging.error(err) logging.error("Error Found, ending returnUsersListGeneratorExtended!") break logging.info("We have {} user rows in the end".format(len(users))) yield users # Returns User List Token # Parameters: # dDomain = Google Apps Domain # SAJson = Service Account Json Authentication File # SADelegated = SuperAdmin Delegated Account # # Generates a list of users in a given GAps domain # # Returns: JSON list of users, for a give date, with the defined fields def returnUsersListToken(dDomain, SAJson, SADelegated, page_token): dDate = date.today().strftime("%Y-%m-%d") try: reports = createReportObject(cfg['scopes']['admin_directory'], 'admin', 'directory_v1', SAJson, SADelegated) except Exception as err: bvi_log(date=dDate, resource='users_list', message_id='invalid_credential', message=err, regenerate=True) raise err # AVAILABLE FIELDS: https://developers.google.com/admin-sdk/directory/v1/reference/users#resource fields = 'nextPageToken' tokens = [] while True: try: if page_token == '': request = reports.users().list(domain=dDomain, maxResults=maxResultsPage, fields=fields) else: request = reports.users().list(domain=dDomain, pageToken=page_token, maxResults=maxResultsPage, fields=fields) results = execute_request_with_retries(request) if 'nextPageToken' in results: tokens = results['nextPageToken'] page_token = results['nextPageToken'] yield tokens else: break except DeadlineExceededError as err: logging.error(err) logging.error("Retrying!") except Exception as err: logging.error(err) logging.error("Error Found, ending returnUsersListToken!") bvi_log(date=dDate, resource='users_list', message_id='users_list_api_token', message=err, regenerate=True) tokens = [] break yield tokens # Returns User List Page Token # Parameters: # dDomain = Google Apps Domain # SAJson = Service Account Json Authentication File # SADelegated = SuperAdmin Delegated Account # # Generates a list of users in a given GSuite domain # # Returns: JSON list of users, for a give date, with the defined fields def returnUserListPageToken(token, dDay, dDomain, SAJson, SADelegated): dDate = dDay page_token = token users = [] try: reports = createReportObject(cfg['scopes']['admin_directory'], 'admin', 'directory_v1', SAJson, SADelegated) except Exception as err: bvi_log(date=dDate, resource='users_list', message_id='invalid_credential', message=err, regenerate=True) raise err # AVAILABLE FIELDS: https://developers.google.com/admin-sdk/directory/v1/reference/users#resource fields = 'nextPageToken,users(creationTime,customerId,emails,lastLoginTime,orgUnitPath,primaryEmail)' try: if page_token == '': request = reports.users().list(domain=dDomain, maxResults=maxResultsPage, fields=fields) else: request = reports.users().list(domain=dDomain, pageToken=page_token, maxResults=maxResultsPage, fields=fields) results = execute_request_with_retries(request) if 'users' in results: users = results['users'] for user_item in users: user_item[u'date'] = dDate except DeadlineExceededError as err: logging.error(err) logging.error("Retrying!") except Exception as err: logging.error(err) logging.error(dDomain) logging.error("Error Found, ending returnUserListPageToken!") bvi_log(date=dDate, resource='users_list', message_id='users_list_api', message=err, regenerate=True) logging.info("We have {} user rows in the end".format(len(users))) yield users # Returns User Usage Token # Parameters: # dDay = A given date # SAJson = Service Account Json Authentication File # SADelegated = SuperAdmin Delegated Account # # Generates a User Usage Report for a given day -3 (Due to data availability) # # Returns: JSON with list of page tokens def returnUserUsageToken(dDay, SAJson, SADelegated, page_token): tokens = [] try: reports = createReportObject(cfg['scopes']['admin_report'], 'admin', 'reports_v1', SAJson, SADelegated) except Exception as err: bvi_log(date=dDay, resource='user_usage', message_id='invalid_credential', message=err, regenerate=True) raise err # AVAILABLE FIELDS: https://developers.google.com/admin-sdk/reports/v1/reference/usage-ref-appendix-a/users fields = 'nextPageToken' pages = 0 while True: try: if page_token == '': request = reports.userUsageReport().get(userKey='all', date=dDay, fields=fields, maxResults=maxResultsPage_UserUsage) else: request = reports.userUsageReport().get(userKey='all', date=dDay, fields=fields, pageToken=page_token, maxResults=maxResultsPage_UserUsage) results = execute_request_with_retries(request) pages += 1 if 'nextPageToken' in results: tokens = results['nextPageToken'] page_token = results['nextPageToken'] yield tokens else: break except DeadlineExceededError as err: logging.error(err) logging.error("Retrying!") except Exception as err: logging.error(err) logging.error("Error Found, ending returnUserUsageToken !") tokens = [] bvi_log(date=dDay, resource='user_usage', message_id='user_usage_api', message=err, regenerate=True) break logging.info("We have {} user_usage pages in the end".format(pages)) logging.info(tokens) yield tokens # Returns User Usage Report Page Token # Parameters: # dDomain = Google Apps Domain # SAJson = Service Account Json Authentication File # SADelegated = SuperAdmin Delegated Account # # Generates a User Usage Report for a given day -3 (Due to data availability) # # Returns: JSON with the User Usage Metrics def returnUserUsagePageToken(token, dDay, SAJson, SADelegated): dDate = dDay page_token = token user_usage = [] try: reports = createReportObject(cfg['scopes']['admin_report'], 'admin', 'reports_v1', SAJson, SADelegated) except Exception as err: bvi_log(date=dDay, resource='user_usage', message_id='invalid_credential', message=err, regenerate=True) raise err # AVAILABLE FIELDS: https://developers.google.com/admin-sdk/reports/v1/reference/usage-ref-appendix-a/users fields = 'nextPageToken,usageReports(date,entity,parameters)' try: if page_token == '': request = reports.userUsageReport().get(userKey='all', date=dDay, fields=fields, maxResults=maxResultsPage_UserUsage) else: request = reports.userUsageReport().get(userKey='all', date=dDay, fields=fields, pageToken=page_token, maxResults=maxResultsPage_UserUsage) results = execute_request_with_retries(request) user_usage = results.get('usageReports', []) except Exception as err: logging.error(err) logging.error("Error Found, ending returnUserUsagePageToken!") bvi_log(date=dDay, resource='user_usage', message_id='user_usage_api', message=err, regenerate=True) logging.info("We have {} user_usage rows in the end".format(len(user_usage))) yield user_usage # Returns Activities Token # Parameters: # dDay = A given date # appName # SAJson = Service Account Json Authentication File # SADelegated = SuperAdmin Delegated Account # # Generates a Activities Report for a given day -3 (Due to data availability) and App # # Returns: JSON with list of page tokens def returnActivitiesToken(dDay, appName, SAJson, SADelegated, page_token): tokens = [] startTime = dDay + 'T00:00:00.000Z' endTime = dDay + 'T23:59:59.999Z' try: reports = createReportObject(cfg['scopes']['audit_log'], 'admin', 'reports_v1', SAJson, SADelegated) except Exception as err: bvi_log(date=dDay, resource='activities', message_id='invalid_credential', message=err, regenerate=True) raise err fields = 'nextPageToken' while True: try: if page_token == '': request = reports.activities().list(userKey='all', applicationName=appName, startTime=startTime, endTime=endTime, fields=fields, maxResults=maxResultsPage) else: request = reports.activities().list(userKey='all', applicationName=appName, startTime=startTime, endTime=endTime, fields=fields, pageToken=page_token, maxResults=maxResultsPage) results = execute_request_with_retries(request) if 'nextPageToken' in results: tokens = results['nextPageToken'] page_token = results['nextPageToken'] yield tokens else: break except DeadlineExceededError as err: logging.error(err) logging.error("Retrying!") except Exception as err: logging.error(err) logging.error("Error Found, ending returnActivitiesToken !") tokens = [] bvi_log(date=dDay, resource='activities', message_id='activities_api', message=err, regenerate=True) break yield tokens # Returns Activities Report Page Token # Parameters: # token # appName # dDomain = Google Apps Domain # SAJson = Service Account Json Authentication File # SADelegated = SuperAdmin Delegated Account # # Generates a Activities Report for a given day -3 (Due to data availability) and App # # Returns: JSON with the User Usage Metrics def returnActivitiesPageToken(token, appName, dDay, SAJson, SADelegated): startTime = dDay + 'T00:00:00.000Z' endTime = dDay + 'T23:59:59.999Z' page_token = token activities = [] try: reports = createReportObject(cfg['scopes']['audit_log'], 'admin', 'reports_v1', SAJson, SADelegated) except Exception as err: bvi_log(date=dDay, resource='activities', message_id='invalid_credential', message=err, regenerate=True) raise err # AVAILABLE FIELDS: https://developers.google.com/admin-sdk/reports/v1/reference/activity-ref-appendix-a/admin-event-names and more. fields = 'items(actor,events,id),nextPageToken' try: if page_token == '': request = reports.activities().list(userKey='all', applicationName=appName, startTime=startTime, endTime=endTime, fields=fields, maxResults=maxResultsPage) else: request = reports.activities().list(userKey='all', applicationName=appName, startTime=startTime, endTime=endTime, fields=fields, pageToken=page_token, maxResults=maxResultsPage) results = execute_request_with_retries(request) logging.info(results) activities = results.get('items', []) except Exception as err: logging.error(err) logging.error("Error Found, ending returnActivitiesPageToken!") bvi_log(date=dDay, resource='activities', message_id='activities_api', message=err, regenerate=True) logging.info("We have {} activities rows in the end".format(len(activities))) yield activities def get_dateref_or_from_cron(dateref): if dateref == "from_cron": report_date = date.today() - timedelta(days=4) dateref = report_date.strftime("%Y-%m-%d") return dateref class PrintMain(webapp2.RequestHandler): def get(self): logging.info('Main') self.response.write(cfg['version']) self.response.write("<p>Task Max Pages: {}</p>".format(cfg['task_management']['max_pages'])) self.response.write("<p>Task Page Size: {}</p>".format(cfg['task_management']['page_size'])) if cfg['plan'] == 'Business': self.response.write("<p>Task Page Size User_Usage: {}</p>".format(cfg['task_management']['page_size_user_usage'])) application = webapp2.WSGIApplication([('/', PrintMain)], debug=True)
<gh_stars>0 # -*- coding: utf-8 -*- # ------------------------------------------------------------------------------------------------- # -- IdentifyFeatures.py # Encapusaltes IdentifyFeatures REST-API from API3 geo.admin.ch # -- Author: flu, 09.11.2020 # -- commandline: # ------------------------------------------------------------------------------------------------- # -- History # -- 0.2: flu, 20.11.2020 some issue with json, gets also info if no parz found # ------------------------------------------------------------------------------------------------- import json import os # import re import sys import requests import requests.packages.urllib3.exceptions as r_ex class IdentifyFeatures: """ Shows how to get a json from https://api3.geo.admin.ch/rest/services/api/MapServer/identify Needs a point coordinate and a distance. The distance ist needed to caluculate a square with a side length twice the distance an the point in the center. This square is used as a intersecting envelope geometry with no buffer Needs a IdentifyFeatures.json. In this file there is a dictionary with 3 [T/I/P]config. Each config has the query rest endpoint to the tlm_hoheitsgebiet, tlm_bezirksgebiet, tlm_kantonsgebiet. These endpoints belong to ESRI Featureservices based on https://shop.swisstopo.admin.ch/en/products/landscape/boundaries3D. This data are needed because there is no information in geo.admin.ch about the relation between Gemeinde belongs to Bezirk belongs to Kanton. Each config has the query rest endpoint to the url_ubm_AdminUnit_1 to url_ubm_AdminUnit_6. These endpoints belong to ESRI Featureservices based on https://eurogeographics.org/maps-for-europe/ebm/ (June 2021 is this dataset not free for use) These data are needed to get information about points situated outside of Switzerland. This dataset has ETRS89 as source reference system. We assume ETRS89 as equal to WGS84 for switzerland and the border are. To transform the coordinates from CHLV95 to ETRS89 we use the reframe service from swisstopo https://geodesy.geo.admin.ch/reframe/lv95towgs84 Attributes: point: Where to identify, coordinate pair. First value ist east second is north in LV95 (EPSG code 2056). Default is (2600000.000,1200000.000) distance: half length of the square in meter. Default = 0.0 layers: comma separated list of technical layer names like ch.swisstopo.pixelkarte-pk25.metadata """ def __init__( self, pt=(2600000.000, 1200000.000), distance=0.0, layers="ch.swisstopo.pixelkarte-pk25.metadata", ): """ Inits IdentifyFeatures sets the attributes """ _cfg = self.__get_settings() self.__pt = pt self.__distance = distance self.__layers = layers try: self.__envelope = ( str(pt[0] - distance) + "," + str(pt[1] - distance) + "," + str(pt[0] + distance) + "," + str(pt[1] + distance) ) except Exception as e: raise e self.__url_api3_identify = _cfg["api3_identify"] self.__url_api3_find = _cfg["api3_find"] self.__url_api3_search_server = _cfg["api3_search"] self.__url_lv95towgs84 = _cfg["lv95towgs84"] self.__url_tlm_hoheitsgebiet = _cfg["T"]["tlm_hoheitsgebiet"] self.__url_tlm_bezirksgebiet = _cfg["T"]["tlm_bezirksgebiet"] self.__url_tlm_kantonsgebiet = _cfg["T"]["tlm_kantonsgebiet"] self.__url_ebm_adminunit_1 = _cfg["I"]["ebm_adminunit_1"] self.__url_ebm_adminunit_2 = _cfg["I"]["ebm_adminunit_2"] self.__url_ebm_adminunit_3 = _cfg["I"]["ebm_adminunit_3"] self.__url_ebm_adminunit_4 = _cfg["I"]["ebm_adminunit_4"] self.__url_ebm_adminunit_5 = _cfg["I"]["ebm_adminunit_5"] self.__url_ebm_adminunit_6 = _cfg["I"]["ebm_adminunit_6"] self.__params = { "geometryType": "esriGeometryEnvelope", "geometry": self.__envelope, "imageDisplay": "0,0,0", "mapExtent": "0,0,0,0", "tolerance": "0", "layers": "all:" + layers, "returnGeometry": "false", "sr": 2056, } _status, _results = self.api3identify() if len(_results["results"]) > 0: self.__lk25 = _results["results"][0]["featureId"] else: self.__lk25 = "" @property def getpt(self): return self.__pt @property def getpt_east(self): return self.__pt[0] @property def getpt_north(self): return self.__pt[1] @property def getdistance(self): return self.__distance @property def getlayers(self): return self.__layers @property def getlayerlist(self): return list(self.__layers.split(",")) @property def getenvelope(self): return self.__envelope @property def getlk25(self): return self.__lk25 @property def __getproxy(self): # _proxy = "proxy.admin.ch:8080" _proxy = "proxy-bvcol.admin.ch:8080" # _proxy = "lf00002a.adb.intra.admin.ch" return {"http": _proxy, "https": _proxy} def api3identify(self): """ gets the json from api3.geo.admin.ch identify Args: - Returns: json from api3.geo.admin.ch identify Raises: JSONDecodeError: If the data being deserialized is not a valid JSON document. """ # _proxy = "proxy.admin.ch:8080" # _proxy_dict = {"http": _proxy, "https": _proxy} # _req = requests.get( # url=self.__url_api3_identify, # proxies=self.__getproxy, # params=self.__params, # verify=False, # ) # return _req.status_code, json.loads(_req.content) _status, _json = self.__get_jsonfromurl_proxy( self.__url_api3_identify, self.__params ) return _status, json.loads(_json) def getparzinfo(self, distance=None): """ Gets infos to the parzelle in a array of dictionary (ParzNummer,egris_egrid,Gemeinde, BFSNummer,Bezirk,Kanton) Args: optional search distance, overwrites the default distance Returns: array of dictionary Raises: - """ _parzinfos = [] _envelope = "" if distance is None: _envelope = self.__envelope else: _envelope = ( str(self.getpt_east - distance) + "," + str(self.getpt_north - distance) + "," + str(self.getpt_east + distance) + "," + str(self.getpt_north + distance) ) # serch parcels _status_parz, _parzarr = self.__identify( _envelope, "ch.kantone.cadastralwebmap-farbe" ) for _parz in _parzarr["results"]: _egris_egrid = _parz["attributes"]["egris_egrid"] _parcelnumber = _parz["attributes"]["number"] _kt = _parz["attributes"]["label"] _ok, _gdename = self.__search_gde(_egris_egrid, _kt) if _ok: _parzdict = {} _parzdict.update({"egris_egrid": _egris_egrid}) _parzdict.update({"ParzNummer": _parcelnumber}) _parzdict.update({"Kanton": _kt}) _parzdict.update({"Gemeinde": _gdename}) _parzdict.update({"BFSNummer": ""}) _parzinfos.append(_parzdict) # search municipality _stauts_gde, _gdearr = self.__identify( _envelope, "ch.swisstopo.swissboundaries3d-gemeinde-flaeche.fill" ) for _gde in _gdearr["results"]: _gdename = _gde["attributes"]["gemname"] _kt = _gde["attributes"]["kanton"] _bfsnr = _gde["id"] _found = False # add municipality if no parcel found for _idx in range(len(_parzinfos)): if _parzinfos[_idx]["Kanton"] == _kt and ( _parzinfos[_idx]["Gemeinde"] == _gdename or _parzinfos[_idx]["Gemeinde"] == "" ): _parzinfos[_idx].update({"BFSNummer": _bfsnr}) _found = True if not _found: _parzdict = {} _parzdict.update({"egris_egrid": ""}) _parzdict.update({"ParzNummer": ""}) _parzdict.update({"Kanton": _kt}) _parzdict.update({"Gemeinde": _gdename}) _parzdict.update({"BFSNummer": _bfsnr}) _parzinfos.append(_parzdict) if len(_parzinfos) == 0: _parzdict = {} _parzdict.update({"egris_egrid": ""}) _parzdict.update({"ParzNummer": ""}) _parzdict.update({"Kanton": _kt}) _parzdict.update({"Gemeinde": _gdename}) _parzdict.update({"BFSNummer": _bfsnr}) _parzinfos.append(_parzdict) # append bezirk and country for _idx in range(len(_parzinfos)): if _parzinfos[_idx]["BFSNummer"] == "": _parzinfos[_idx]["BFSNummer"] = self.__find_gde_bfsnr( _parzinfos[_idx]["Gemeinde"], _parzinfos[_idx]["Kanton"] )[1] _beznr, _ktnr, _icc = self.__query_hoheitsgebiet( _parzinfos[_idx]["BFSNummer"] ) if _beznr is None: _parzinfos[_idx].update({"Bezirk": ""}) else: _parzinfos[_idx].update({"Bezirk": self.__query_bezirk_name(_beznr)}) _parzinfos[_idx].update({"Land": _icc}) _parzinfos[_idx].update({"LK25NR": self.__lk25}) _ausland = False if len(_parzinfos) == 0: _ausland = True _ebms = [] _counter = 0 while len(_ebms) == 0 and _ausland and _counter < 10: _ebms = self.getebm(_counter * 1.0) _counter += 1 if len(_ebms) > 0: for _ebm in _ebms: _parzinfos.append(_ebm) return json.dumps(_parzinfos) def getebm(self, dist=0.0): _resultarr = [] _params = {} _params.update({"easting": self.__pt[0]}) _params.update({"northing": self.__pt[1]}) _params.update({"format": "json"}) _status, _json = self.__get_jsonfromurl_proxy(self.__url_lv95towgs84, _params) _json = json.loads(_json) _etrs89_east = _json["easting"] _etrs89_north = _json["northing"] # get Country _params.clear() _params.update({"geometry": "{0},{1}".format(_etrs89_east, _etrs89_north)}) _params.update({"geometryType": "esriGeometryPoint"}) if dist == 0.0: if self.getdistance == 0.0: _params.update({"distance": 0.001}) else: _params.update({"distance": self.getdistance}) else: _params.update({"distance": dist}) _params.update({"units": "esriSRUnit_Meter"}) _params.update({"outFields": "*"}) _params.update({"returnGeometry": "false"}) # _params.update({"distance": 1}) # _params.update({"units": "esriSRUnit_Meter"}) _params.update({"f": "json"}) # municipalities DE _json = self.__get_jsonfromurl(self.__url_ebm_adminunit_6, _params)["features"] for _gdede in _json: if _gdede["attributes"]["ICC"] in ["DE"]: _resultarr.append(self.__init_ebmgde(_gdede["attributes"])) # municipalities FR _json = self.__get_jsonfromurl(self.__url_ebm_adminunit_5, _params)["features"] for _gdede in _json: if _gdede["attributes"]["ICC"] in ["FR"]: _resultarr.append(self.__init_ebmgde(_gdede["attributes"])) # municipalities not DE | FR | CH | LI _json = self.__get_jsonfromurl(self.__url_ebm_adminunit_4, _params)["features"] for _gdede in _json: if _gdede["attributes"]["ICC"] not in ["DE", "FR", "CH", "LI"]: _resultarr.append(self.__init_ebmgde(_gdede["attributes"])) # bezirk _json = self.__get_jsonfromurl(self.__url_ebm_adminunit_3, _params)["features"] for _bezirk in _json: if _bezirk["attributes"]["ICC"] not in ["CH", "LI"]: _shnbezirk = _bezirk["attributes"]["SHN"].rstrip("0") for _idx in range(0, len(_resultarr)): if _resultarr[_idx]["SHN"].startswith(_shnbezirk): _resultarr[_idx]["Bezirk"] = _bezirk["attributes"]["NAMN"] # kanton _json = self.__get_jsonfromurl(self.__url_ebm_adminunit_2, _params)["features"] for _kanton in _json: if _kanton["attributes"]["ICC"] not in ["CH", "LI"]: _shnkanton = _kanton["attributes"]["SHN"].rstrip("0") for _idx in range(0, len(_resultarr)): if _resultarr[_idx]["SHN"].startswith(_shnkanton): _resultarr[_idx]["Kanton"] = _kanton["attributes"]["NAMN"] # set BFSNummer per country for _idx in range(0, len(_resultarr)): if _resultarr[_idx]["Land"] == "IT": _resultarr[_idx]["BFSNummer"] = 9996 elif _resultarr[_idx]["Land"] == "FR": _resultarr[_idx]["BFSNummer"] = 9997 elif _resultarr[_idx]["Land"] == "DE": _resultarr[_idx]["BFSNummer"] = 9998 elif _resultarr[_idx]["Land"] == "AT": _resultarr[_idx]["BFSNummer"] = 9999 return _resultarr def __init_ebmgde(self, jsondata): """ returns dictionary with "gemeinde" Args: jsondata: attribut tag from query service Returns: dictionary Raises: """ _parzdict = {} _parzdict.update({"egris_egrid": ""}) _parzdict.update({"ParzNummer": ""}) _parzdict.update({"Kanton": ""}) _parzdict.update({"Gemeinde": jsondata["NAMN"]}) _parzdict.update({"BFSNummer": ""}) _parzdict.update({"Bezirk": ""}) _parzdict.update({"Land": jsondata["ICC"]}) _parzdict.update({"SHN": jsondata["SHN"]}) _parzdict.update({"LK25NR": self.__lk25}) return _parzdict def __get_settings(self): """ Reads the json file with configuration's for T|I and P Args: - Returns: dictionary with the settings Raises: JSONDecodeError: If the data being deserialized is not a valid JSON document. IOError: if the config json file file is missing """ _setting_filename = os.path.join( os.path.dirname(sys.argv[0]), "IdentifyFeatures.json" ) try: with open(_setting_filename, "r") as _myfile: _data = _myfile.read() except IOError: raise # parse file try: return json.loads(_data) except json.JSONDecodeError: raise except Exception: raise def __get_jsonfromurl(self, url, parameter): """ Gets a json from a ArcGISServerURL via a get with parameterlist Args: url: the url to call paramter: paramterd ictionary Returns: Json Raises: ConnectionError if there is no connction to the rest end point """ requests.packages.urllib3.disable_warnings(r_ex.InsecureRequestWarning) session = requests.Session() session.trust_env = False try: req = session.get( url=url, params=parameter, auth=requests.auth.HTTPDigestAuth(os.environ["USERNAME"], False), verify=False, ) except ConnectionError: raise ConnectionError( "Could not get connection to {0} with parameterset {1}".format( url, str(parameter) ) ) except Exception: raise return req.json() def __get_jsonfromurl_proxy(self, url, parameter): """ Gets a json from a serverurl via a get with parameterlist and via proxy Args: url: the url to call paramter: paramterd ictionary Returns: Json Raises: ConnectionError if there is no connction to the rest end point """ requests.packages.urllib3.disable_warnings(r_ex.InsecureRequestWarning) session = requests.Session() session.trust_env = False try: req = session.get( url=url, proxies=self.__getproxy, params=parameter, verify=False, ) except ConnectionError: raise ConnectionError( "Could not get connection to {0} with parameterset {1}".format( url, str(parameter) ) ) except Exception: raise return req.status_code, req.content def __query_bezirk_name(self, bezirkid=0): """ Gets the name of the bezirk from bezirksgebiet if there is no bezirk, enpty string Args: bezirksnummer Returns: Bezirksname Raises: - """ if bezirkid is not None: _params = {} _params.update( {"where": "BEZIRKSNUMMER={0} and BEZIRK_TEIL < 2".format(bezirkid)} ) _params.update({"outFields": "NAME,BEZIRK_TEIL"}) _params.update({"returnGeometry": "false"}) _params.update({"f": "json"}) _req = self.__get_jsonfromurl(self.__url_tlm_bezirksgebiet, _params) _features = _req["features"] if len(_features) == 0: return "" elif len(_features) == 1: return _features[0]["attributes"]["NAME"] else: raise ValueError("To many districts found") else: return "" def __query_hoheitsgebiet(self, bfsnummer=0): """ Gets the bezirksnummer, kantonsnummer and ICC county code from the hoheitsgebiet Args: gemeinde bfsnummer Returns: bezirksnummer, kantonsnummer, ICC county code Raises: - """ _params = {} _params.update({"where": "BFS_NUMMER={0} and GEM_TEIL < 2".format(bfsnummer)}) _params.update({"outFields": "GEM_TEIL,BEZIRKSNUMMER,KANTONSNUMMER,ICC"}) _params.update({"returnGeometry": "false"}) _params.update({"f": "json"}) _req = self.__get_jsonfromurl(self.__url_tlm_hoheitsgebiet, _params) _features = _req["features"] if len(_features) == 1: return ( _features[0]["attributes"]["BEZIRKSNUMMER"], _features[0]["attributes"]["KANTONSNUMMER"], _features[0]["attributes"]["ICC"], ) else: raise ValueError("To many municipalities found") def __find_gde_bfsnr(self, gde="", kt=""): """ Gets the gemeinde bfsnummer from ch.swisstopo.swissboundaries3d-gemeinde-flaeche.fill Args: gemeinde name, kantonskuerzel Returns: true if found, false if not found and gemeinde bfsnummer Raises: - """ _params = {} _params.update( {"layer": "ch.swisstopo.swissboundaries3d-gemeinde-flaeche.fill"} ) _params.update({"searchText": "{0}".format(gde)}) _params.update({"searchField": "gemname"}) _params.update({"returnGeometry": "false"}) _req = requests.get( url=self.__url_api3_find, proxies=self.__getproxy, params=_params, verify=False, ) if _req.status_code == 200: _json = json.loads(_req.content) _results = _json["results"] # print(_results) if len(_results) == 0: return False, "gemname not found" else: for _ele in _results: if _ele["attributes"]["kanton"] == kt: return True, _ele["id"] return False, "gemname in kanton not found" else: return False, _req.content def __search_gde(self, location, kt): """ Gets the Gemeindename Args: egris_egrid Returns: True if found, false if not found and gemeindename Raises: - """ _params = {} _params.update({"searchText": "{0}".format(location)}) _params.update({"type": "locations"}) _req = requests.get( url=self.__url_api3_search_server, proxies=self.__getproxy, params=_params, verify=False, ) if _req.status_code == 200: _json = json.loads(_req.content) _results = _json["results"] if len(_results) == 1: # _clean = re.compile("<.*?>") # return ( # True, # re.sub(_clean, "", _results[0]["attrs"]["label"].split(" ")[0]), # ) _label = _results[0]["attrs"]["label"] return True, _label[3 : _label.find("</b>")] # _sa = _results[0]["attrs"]["label"].split(" ") # if len(_sa) == 6: # return True, re.sub(_clean, "", _sa[0]) # elif len(_sa) == 7: # return True, re.sub(_clean, "", "{0} {1}".format(_sa[0], _sa[1])) # elif len(_sa) == 8: # return ( # True, # re.sub( # _clean, "", "{0} {1} {2}".format(_sa[0], _sa[1], _sa[2]) # ), # ) # elif len(_sa) == 9: # return ( # True, # re.sub( # _clean, # "", # "{0} {1} {2} {3}".format(_sa[0], _sa[1], _sa[2], _sa[3]), # ), # ) # # else: # False, "could not decode label" else: return False, "egris_egrid not found" else: return False, _req.content def __identify(self, envelope, layer): """ calls api3 identify with one layer Args: envelope, search box layer, technical layername from map.geo.admin.ch Returns: statuscode, 200 if ok json result Raises: - """ _parms1 = self.__params _parms1["geometry"] = envelope _parms1["layers"] = "all:{0}".format(layer) _req = requests.get( url=self.__url_api3_identify, proxies=self.__getproxy, params=_parms1, verify=False, ) return _req.status_code, json.loads(_req.content)
<reponame>jzuhusky/zbaseball-client from datetime import datetime from typing import List import requests from .constants import GAME_TYPES from .exceptions import ( APIException, ClientException, GameNotFoundException, LoginError, PaymentRequiredException, PlayerNotFoundException, TooManyRequestsException, TeamNotFoundException, ParkNotFoundException, ) class ZBaseballDataClient(object): def __init__( self, username=None, password=<PASSWORD>, api_url="https://www.zbaseballdata.com" ): self._username = username self._password = password self._api_url = api_url self._session = requests.Session() self._session.headers.update({"Accept": "application/json"}) self._login() def _get(self, *args, **kwargs): """Get wrapper to catch and retry all HTTP 401s (token may be stale)""" response = self._session.get(*args, **kwargs) if response.status_code == 401: self._login() response = self._session.get(*args, **kwargs) elif response.status_code == 429: msg = "API query rate exceeded" raise TooManyRequestsException(msg) elif response.status_code == 402: msg = response.json()["detail"] raise PaymentRequiredException(msg) return response def _login(self): """Use credentials to grab a new api token""" self._session.headers.pop("Authorization", None) login_endpoint = self._api_url + "/api/auth/login/" response = self._session.post( url=login_endpoint, data={"username": self._username, "password": self._password}, ) if response.status_code == 200: token = response.json()["token"] self._session.headers.update({"Authorization": "Token {}".format(token)}) else: msg = response.json()["msg"] raise LoginError(msg) def _logout(self): """Indicate to the API we are done with our current token""" login_endpoint = self._api_url + "/api/auth/logout/" self._session.post(url=login_endpoint) del self._session.headers["Authorization"] def get_game(self, game_id): """Retrieve data for a specific game Args: game_id: str, the unique identifier for a particular game. E.g. "NYA192104130" Returns: A dict with details about that particular game. Fields including but not limited to: time, attendance, umpires, winning pitcher, losing pitcher, game site, weather, wind dir, temperature, game duration, date and a few more. """ game_endpoint = self._api_url + "/api/v1/games/{}/".format(game_id) response = self._get(url=game_endpoint) if response.status_code == 404: raise GameNotFoundException(response.json()["detail"]) elif response.status_code != 200: msg = "Received HTTP status {} when fetching game_id={}".format( response.status_code, game_id ) raise APIException(msg) game = response.json() game["date"] = datetime.strptime(game["date"], "%Y-%m-%d").date() return game def get_game_events(self, game_id): """Get a list of play-by-play events for a specific game""" game_endpoint = self._api_url + "/api/v1/games/{}/events/".format(game_id) response = self._get(url=game_endpoint) if response.status_code == 404: raise GameNotFoundException(response.json()["detail"]) elif response.status_code != 200: msg = "Received HTTP status {} when fetching events for game_id={}".format( response.status_code, game_id ) raise APIException(msg) return response.json() def get_games( self, year: int = None, team_id: str = None, start_date: str = None, end_date: str = None, game_type: str = None, ): """Get games & allow some filters Args: year: int or None, you may filter games by year (or season if you prefer). The API will return all regular season games as well as post season games. The API does not distinguish between regular season games and post-season. team_id: str or None, filter games by a teams 3 character "team-id". E.g. "NYA" NB! 3 Character team-id's are NOT neccessarily unique! Specifically, for MIL and HOU, there are 2 "teams" with each of those ID's. Generally, this happens when a particular team switches leagues from AL to NL or vice versa. start_date: str, e.g. 2019-01-01 only return games after this date end_date: str, only return games before this date game_type: str, filter based on regular season games, postseason, allstar etc. SEE constants.py for the full list of options. Use POST for only postseason games, REG for only regular season games, None for all games. Returns: a generator of dicts, such that each dict has some simple facts about each game. E.g. { "game_id": "NYA192104140", "date": "1921-04-14", "start_time": null, "home_team": 9, "away_team": 98 } "home_team" and "away_team" are the UNIQUE team identifiers. Details about a team can be found using the teams API or the "get_teams" client method. """ filters = [] if year: filters.append("year={}".format(year)) if team_id: filters.append("team-id={}".format(team_id)) if start_date: filters.append("start-date={}".format(start_date)) if end_date: filters.append("end-date={}".format(end_date)) if game_type: if game_type != "POST" and game_type not in GAME_TYPES: msg = "game_type must be 'POST' or one of {}".format(GAME_TYPES) raise ClientException(msg) filters.append("game-type={}".format(game_type)) games_endpoint = self._api_url + "/api/v1/games/" if len(filters) > 0: games_endpoint += "?" + "&".join(filters) response = self._get(url=games_endpoint) if response.status_code == 400: msg = response.json()["detail"] raise APIException(msg) elif response.status_code != 200: msg = "Received HTTP status {} when fetching games".format( response.status_code ) raise APIException(msg) data = response.json() while len(data["results"]) > 0: for game in data["results"]: yield game next_url = data["next"] if next_url is None: break response = self._get(url=next_url) data = response.json() def get_player(self, retro_id): """Get some basic details about a player""" player_endpoint = self._api_url + "/api/v1/players/{}/".format(retro_id) response = self._get(url=player_endpoint) if response.status_code == 404: msg = "Player with retro-id={} not found.".format(retro_id) raise PlayerNotFoundException(msg) elif response.status_code != 200: msg = "Received HTTP status {} when fetching player w/ retro-id={}".format( response.status_code, retro_id ) raise APIException(msg) player_data = response.json() player_data["debut"] = datetime.strptime( player_data["debut"], "%Y-%m-%d" ).date() return player_data def get_players(self, search=None): """Get players, with some searching capacity Args: search: str | None, an optional parameter that you can search for players on. The search term will return players with either first-names, last-names or retro_ids that are "LIKE" (read startswith) the search term. Returns: a generator of player-dict/objects, where each dict has first-name, last-name unique "retro_id" and the player's MLB debut. """ player_endpoint = self._api_url + "/api/v1/players/" if search: search.replace(" ", "%20") player_endpoint += "?search={}".format(search) response = self._get(url=player_endpoint) if response.status_code != 200: msg = "Received HTTP status {} when fetching players.".format( response.status_code ) raise APIException(msg) data = response.json() while len(data["results"]) > 0: for player in data["results"]: player["debut"] = datetime.strptime(player["debut"], "%Y-%m-%d").date() yield player next_url = data["next"] if next_url is None: break response = self._get(url=next_url) data = response.json() def get_parks(self, city=None, state=None, league=None): """Get gen of ballparks known to the retrosheet universe""" query_params = [] if city: query_params.append("city={}".format(city)) if state: query_params.append("state={}".format(state)) if league: query_params.append("league={}".format(city)) if len(query_params) > 0: query_string = "?" + "&".join(query_params) else: query_string = "" parks_endpoint = self._api_url + "/api/v1/parks/" + query_string response = self._get(parks_endpoint) if response.status_code != 200: msg = "Received HTTP status {} when fetching parks".format( response.status_code ) raise APIException(msg) data = response.json() while len(data["results"]) > 0: for park in data["results"]: park["start_date"] = datetime.strptime( park["start_date"], "%Y-%m-%d" ).date() if park["end_date"] is not None: park["end_date"] = datetime.strptime( park["end_date"], "%Y-%m-%d" ).date() yield park next_url = data["next"] if next_url is None: break response = self._get(url=next_url) data = response.json() def get_park(self, park_id): """Get a specific park object""" park_endpoint = self._api_url + "/api/v1/parks/{}".format(park_id) response = self._get(url=park_endpoint) if response.status_code == 404: msg = "Park with park-id={} not found.".format(park_id) raise ParkNotFoundException(msg) elif response.status_code != 200: msg = "Received HTTP status {} when fetching park w/ park-id={}".format( response.status_code, park_id ) raise APIException(msg) park_data = response.json() park_data["start_date"] = datetime.strptime( park_data["start_date"], "%Y-%m-%d" ).date() if park_data["end_date"] is not None: park_data["end_date"] = datetime.strptime( park_data["end_date"], "%Y-%m-%d" ).date() return park_data def get_teams(self, search: str = None, only_active: bool = False): """Get a generator of teams Args: search: str, search parameter which returns teams based on their "nickname" city or string team-id (e.g. NYA). Matches exactly to city and team-id, of partially to nick-name active: bool, only return teams that still exist. Defaults to false Returns: generator of team-object/dicts that match search criteria. """ if only_active: params = "?only-active=1" else: params = "?only-active=0" if search is not None: params += "&search={}".format(search) team_endpoint = self._api_url + "/api/v1/teams/" + params response = self._get(team_endpoint) if response.status_code != 200: msg = "Received HTTP status {} when fetching teams".format( response.status_code ) raise APIException(msg) data = response.json() while len(data["results"]) > 0: for team in data["results"]: yield team next_url = data["next"] if next_url is None: break response = self._get(url=next_url) data = response.json() def get_team(self, int_team_id: int): """Get details about a team""" team_endpoint = self._api_url + "/api/v1/teams/{}/".format(int_team_id) response = self._get(team_endpoint) if response.status_code == 404: msg = "Team with ID: {} not found".format(int_team_id) raise TeamNotFoundException(msg) elif response.status_code != 200: msg = "Received HTTP status {} when fetching team with id: {}".format( response.status_code, int_team_id ) raise APIException(msg) return response.json() def get_player_events( self, retro_id: str, start_date: str = None, end_date: str = None ): """Get paginated events for a player The API exposes an endpoint to filter play-by-play events by player. All events are returned for a specific player, regardless of whether the player was the hitter or the pitcher. Therefore, the user should be careful to understand this point! A user may also filter based on a date window, i.e. return all events within this range of dates, or if only a start_date or end_date is supplied, the events will be bounded by those respective dates. Args: retro_id: str, unique retrosheet ID of the player events should be returned for. start_date: str, YYYY-MM-DD string to return events after end_date: str, YYYY-MM-DD string to return events before Returns: a generator of tuples, which have the form: { "game_id": "HOU201805010", "date": "2018-05-01", "hitter_retro_id": "judga001", "pitcher_retro_id": "verlj001", "pitch_sequence": "F1*BBCS", "event_description": "K", "count_on_play": "22", "inning": 1, "event_count": 1 } """ filters = [] if start_date: filters.append("start-date=" + start_date) if end_date: filters.append("end-date=" + end_date) player_events_endpoint = self._api_url + "/api/v1/players/{}/events/".format( retro_id ) if filters: player_events_endpoint += "?" + "&".join(filters) response = self._get(url=player_events_endpoint) if response.status_code != 200: msg = "Received HTTP status {} when fetching events for player: {}".format( response.status_code, retro_id ) raise APIException(msg) data = response.json() while len(data["results"]) > 0: for event in data["results"]: event["date"] = datetime.strptime(event["date"], "%Y-%m-%d").date() yield event next_url = data["next"] if next_url is None: break response = self._get(url=next_url) data = response.json() def get_batting_stat_split( self, retro_id: str, stats: List[str], agg_by: str, vs_pitcher: str = None, game_type: str = None, pitcher_throw: str = None, start_date: str = None, end_date: str = None, year: int = None, day_or_night: str = None, park_id: str = None, vs_team: str = None, ): """Get batting statistics Args: retro_id: str, for whom we want to get statistics, e.g. judga001 stats: List[str], one or more of H, AB, PA, etc.... see full list in constants.py BATTING_STATS agg_by: str, D (day), M (month), DOW(day of week) etc... for full list see constants.py AGGREGATE_OPTIONS vs_pitcher: str, a retro_id of a player. This will tell the server to return and aggregate data for when this hitter was facing this pitcher. game_type: str, if None, regular and postseason stats are returned. Options are REG, POST, ALCS, ALDS, ALWC, WS... etc... pitcher_throw: str, None or "L" or "R" start_date: str, None or YYYY-MM-DD, return after this date. end_date: str, None or YYYY-MM-DD, return data before this date. year: int, None or some year. Only return data for this year. Returns: a dictionary of the form: Dict[stat, Dict[aggregate, value]. stats = ["HR", "PA"], agg_by="DOW" (day of week) for some player. These values will change if a user is to supply any of the splits (optional parameters) For example: { "HR": { "fri": 10, "mon": 5, "sat": 13, "sun": 7, "thu": 4, "tue": 7, "wed": 8 }, "PA": { "fri": 147, "mon": 108, "sat": 162, "sun": 146, "thu": 106, "tue": 143, "wed": 133 } } """ stat_query_string = "&" + "&".join(["stat={}".format(s) for s in stats]) query_string = ( "?hitter_retro_id={retro_id}&agg_by={agg_by}".format( retro_id=retro_id, agg_by=agg_by ) + stat_query_string ) # Add splits if they're provided if vs_pitcher: query_string += "&vs_pitcher={}".format(vs_pitcher) if game_type: query_string += "&game_type={}".format(game_type) if pitcher_throw: query_string += "&pitcher_throw={}".format(pitcher_throw) if start_date: query_string += "&start_date={}".format(start_date) if end_date: query_string += "&end_date={}".format(end_date) if year: query_string += "&year={}".format(year) if day_or_night: query_string += "&day_or_night={}".format(day_or_night) if park_id: query_string += "&park_id={}".format(park_id) if vs_team: query_string += "&vs_team={}".format(vs_team) stat_split_endpoint = self._api_url + "/api/v1/stats/batting/" + query_string response = self._get(url=stat_split_endpoint) if response.status_code == 400: raise APIException(response.json()["detail"]) elif response.status_code != 200: msg = "Received HTTP status {} when fetching stat split for: {}".format( response.status_code, retro_id ) raise APIException(msg) return response.json() def get_lineup(self, game_id): """Get lineup list given a game_id""" lineup_route = self._api_url + "/api/v1/games/{}/lineup/".format(game_id) response = self._get(lineup_route) if response.status_code == 404: msg = "Game with ID: {} not found".format(game_id) raise TeamNotFoundException(msg) elif response.status_code != 200: msg = "Received HTTP status {} when fetching lineup for game: {}".format( response.status_code, game_id ) raise APIException(msg) return response.json() def get_pitching_stat_split( self, retro_id: str, stats: List[str], agg_by: str = "C", vs_hitter: str = None, game_type: str = None, batter_hand: str = None, year: int = None, start_date: str = None, end_date: str = None, ): """Get pitching stats This client method is the fraternal twin of "get_batting_stat_split". It's pretty much the same, and follows the same rule, except it hits the pitching API. For pitching however, there is is another API for what we call "game level" things, I.e. Wins, Starts, Games, Saves, Losses for pitchers. Naturally, these can't exactly be broken down by inning, or situations with runners on base, so that data comes fromm a second, but very similar, API endpoint. At the time of writing, no client method has been implemented for that, but this will change. This method serves "event level data", i.e. things that can be computed from play by play data. """ stat_query_string = "&" + "&".join(["stat={}".format(s) for s in stats]) query_string = ( "?pitcher_retro_id={retro_id}&agg_by={agg_by}".format( retro_id=retro_id, agg_by=agg_by ) + stat_query_string ) # Add query filters if they're provided if vs_hitter: query_string += "&vs_hitter={}".format(vs_hitter) if game_type: query_string += "&game_type={}".format(game_type) if batter_hand: query_string += "&batter_hand={}".format(batter_hand) if start_date: query_string += "&start_date={}".format(start_date) if end_date: query_string += "&end_date={}".format(end_date) if year: query_string += "&year={}".format(year) stat_split_endpoint = self._api_url + "/api/v1/stats/pitching/" + query_string response = self._get(url=stat_split_endpoint) if response.status_code == 400: raise APIException(response.json()["detail"]) elif response.status_code != 200: msg = "Received HTTP status {} when fetching stat split for: {}".format( response.status_code, retro_id ) raise APIException(msg) return response.json()
""" This module file contains Django-specific helper functions, to help save time when developing with the Django framework. * handle_error - Redirects normal web page requests with a session error, outputs JSON with a status code for API queries. * is_database_synchronized - Check if all migrations have been ran before running code. * model_to_dict - Extract an individual Django model instance into a dict (with display names) * to_json - Convert a model Queryset into a plain string JSON array with display names **Copyright**:: +===================================================+ | © 2019 Privex Inc. | | https://www.privex.io | +===================================================+ | | | Originally Developed by Privex Inc. | | | | Core Developer(s): | | | | (+) Chris (@someguy123) [Privex] | | (+) Kale (@kryogenic) [Privex] | | | +===================================================+ Copyright 2019 Privex Inc. ( https://www.privex.io ) Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import json from django.contrib import messages from django.contrib.messages import add_message from django.http import JsonResponse, HttpRequest from django.http.response import HttpResponseRedirectBase from django.db.migrations.executor import MigrationExecutor from django.db import connections def handle_error(request: HttpRequest, err: str, rdr: HttpResponseRedirectBase, status=400): """ Output an error as either a Django session message + redirect, or a JSON response based on whether the request was for the API readable version (?format=json) or not. Usage: >>> from django.shortcuts import redirect >>> def my_view(request): ... return handle_error(request, "Invalid password", redirect('/login'), 403) :param HttpRequest request: The Django request object from your view :param str err: An error message as a string to display to the user / api call :param HttpResponseRedirectBase rdr: A redirect() for normal browsers to follow after adding the session error. :param int status: The HTTP status code to return if the request is an API call (default: 400 bad request) """ if request.GET.get('format', '') == 'json': return JsonResponse(dict(error=True, message=err), status=status) else: add_message(request, messages.ERROR, err) return rdr def is_database_synchronized(database: str) -> bool: """ Check if all migrations have been ran. Useful for preventing auto-running code accessing models before the tables even exist, thus preventing you from migrating... >>> from django.db import DEFAULT_DB_ALIAS >>> if not is_database_synchronized(DEFAULT_DB_ALIAS): >>> log.warning('Cannot run reload_handlers because there are unapplied migrations!') >>> return :param str database: Which Django database config is being used? Generally just pass django.db.DEFAULT_DB_ALIAS :return bool: True if all migrations have been ran, False if not. """ connection = connections[database] connection.prepare_database() executor = MigrationExecutor(connection) targets = executor.loader.graph.leaf_nodes() return False if executor.migration_plan(targets) else True def model_to_dict(model) -> dict: """1 dimensional json-ifyer for any Model""" from django.forms import model_to_dict as mtd # gets a field on the model, using the display name if available def get_display(model, field, default): method_name = 'get_{}_display'.format(field) return getattr(model, method_name)() if method_name in dir(model) else default return {k: get_display(model, k, v) for k, v in mtd(model).items()} def to_json(query_set) -> str: """Iterate a Django query set and dump to json str""" return json.dumps([model_to_dict(e) for e in query_set], default=str)
"""VPN over DNS protocol utilities.""" import binascii import collections import enum import itertools import regex import struct from vodreassembler import util from vodreassembler import dnsrecord DEFAULT_FQDN_SUFFIX = 'tun.vpnoverdns.com.' class Error(Exception): pass class UnknownVersionError(Error): pass def ipv4_to_bytes(addr): return ipv4_to_chunk(addr)[0] def ipv4_to_chunk(addr): octets = addr.split('.') if len(octets) != 4: raise ValueError('IPv4 addresses must have 4 octets') octets = map(int, octets) try: data = ipv4_to_chunk.packer.pack(*octets) except struct.error as e: raise ValueError('every octet must be within [0,255]') from e length = (data[0] >> 6) & 0x3 offset = (data[0] & 0x3f) * 3 return util.DataChunk(data[1:length+1], offset) ipv4_to_chunk.packer = struct.Struct('!BBBB') def chunk_to_ipv4(chunk): if not isinstance(chunk, util.DataChunk): chunk = util.DataChunk(*chunk) length = len(chunk.data) if length <= 0: raise ValueError('length must be at least 1') elif length > 3: raise ValueError('cannot encode chunks longer than 3 bytes') elif chunk.offset % 3 != 0: raise ValueError('chunk offset must be multiples of 3') elif chunk.offset < 0: raise ValueError('chunk offset cannot be negative') elif chunk.offset // 3 >= 0x3f: raise ValueError('chunk offset cannot exceed {}'.format(0x3f)) return '{}.{}.{}.{}'.format((length << 6) + (chunk.offset // 3), chunk.data[0], chunk.data[1] if length >= 2 else 255, chunk.data[2] if length == 3 else 255) def normalize_fqdn_suffix(fqdn_suffix): if not fqdn_suffix.endswith('.'): fqdn_suffix += '.' if fqdn_suffix.startswith('.'): fqdn_suffix = fqdn_suffix[1:] return fqdn_suffix @enum.unique class QueryType(enum.Enum): unknown = 0 open_ticket = 1 request_data = 2 check_request = 3 fetch_response = 4 close_ticket = 5 @staticmethod def deduce(version, variables, data): if version != '0': raise UnknownVersionError(version) keys = set(variables.keys()) if 'retry' in keys: # ignore clearly optional variables keys.remove('retry') if keys == {'<KEY>'}: return QueryType.open_ticket elif keys == {'<KEY>'}: return QueryType.request_data elif keys == {'ck', 'id'}: return QueryType.check_request elif keys == {'ln', 'rd', 'id'}: return QueryType.fetch_response elif keys == {'ac', 'id'}: return QueryType.close_ticket return QueryType.unknown class Query(collections.namedtuple('Query', ['version', 'type', 'variables', 'payload'])): @classmethod def create(cls, version, variables, payload): querytype = QueryType.deduce(version, variables, payload) variables, payload = cls.normalize_data(querytype, variables, payload) return cls(version, querytype, variables, payload) @staticmethod def normalize_data(querytype, variables, payload): newvars = {} for key in variables: if key in {'id', 'sz', 'rn', 'wr', 'ck', 'ln', 'rd', 'retry'}: newvars[key] = int(variables[key]) elif key in {'bf'}: newvars[key] = binascii.unhexlify(variables[key]) else: newvars[key] = variables[key] return newvars, payload @property def error(self): # Unfortunately, we currently don't have an easy way to find out whether # in a fetch_response payload. Simply wish the byte sequences 69 ## doesn't # appear in the payload. if len(self.payload.data) == 2 and self.payload.data.startswith(b'E'): return self.payload.data[1] or None return None def encode(self, fqdn_suffix=None): fqdn_suffix = normalize_fqdn_suffix(fqdn_suffix or DEFAULT_FQDN_SUFFIX) field_encoders = [ ('retry', str), ('sz', '{:08d}'.format), ('rn', '{:08d}'.format), ('bf', lambda x: binascii.hexlify(x).decode('ascii')), ('wr', '{:08d}'.format), ('ck', '{:08d}'.format), ('ln', '{:08d}'.format), ('rd', '{:08d}'.format), ('ac', None), ('id', '{:08d}'.format), ] def encode_var(field, encoder): if encoder: return field + '-' + encoder(self.variables[field]) return field variables = '.'.join(encode_var(field, encoder) for field, encoder in field_encoders if field in self.variables) return dnsrecord.DnsRecord( variables + '.v' + str(self.version) + '.' + fqdn_suffix, 'IN', 'A', chunk_to_ipv4(self.payload)) class QueryParser: def __init__(self, fqdn_suffix=None): self._suffix = normalize_fqdn_suffix(fqdn_suffix or DEFAULT_FQDN_SUFFIX) self._re = regex.compile( r'''^\s* ((?P<flag>\w+)\.)* # flags ((?P<var>\w+)-(?P<value>\w+)\.)+ # variables v(?P<version>\w+)\. # version {!s} # suffix \s*$'''.format(regex.escape(self._suffix)), regex.VERSION1 | regex.VERBOSE) def parse(self, dns_record): m = self._re.fullmatch(dns_record.fqdn) if not m: raise ValueError( "fqdn '{}' is not in the expected format".format(dns_record.fqdn)) variables = dict.fromkeys(m.captures('flag'), True) variables.update(zip(m.captures('var'), m.captures('value'))) return Query.create(m.group('version'), variables, ipv4_to_chunk(dns_record.value))
# Copyright 2020 EMBL - European Bioinformatics Institute # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from get_assembly_report_url import * import os import pandas import sys import urllib.request import re import json import yaml from retry import retry from __init__ import logger def build_fasta_from_assembly_report(assembly_report_path, assembly_accession, eutils_api_key, directory_path, clear): fasta_path = directory_path + assembly_accession + '.fa' if clear: clear_file_content(fasta_path) written_contigs = get_written_contigs(fasta_path) for chunk in pandas.read_csv(assembly_report_path, skiprows=get_header_line_index(assembly_report_path), dtype=str, sep='\t', chunksize=100): new_contigs_in_chunk = process_chunk(chunk, written_contigs, eutils_api_key, fasta_path) written_contigs.extend(new_contigs_in_chunk) def clear_file_content(path): with open(path, 'w'): pass def get_written_contigs(fasta_path): try: written_contigs = [] match = re.compile(r'>(.*?)\s') with open(fasta_path, 'r') as file: for line in file: written_contigs.extend(match.findall(line)) return written_contigs except FileNotFoundError: logger.info('FASTA file does not exists, starting from scratch') return [] def get_header_line_index(assembly_report_path): try: with open(assembly_report_path) as assembly_report_file_handle: return next(line_index for line_index, line in enumerate(assembly_report_file_handle) if line.lower().startswith("# sequence-name") and "sequence-role" in line.lower()) except StopIteration: raise Exception("Could not locate header row in the assembly report!") def process_chunk(assembly_report_dataframe, written_contigs, eutils_api_key, fasta_path): new_contigs = [] for index, row in assembly_report_dataframe.iterrows(): genbank_accession = row['GenBank-Accn'] refseq_accession = row['RefSeq-Accn'] relationship = row['Relationship'] accession = genbank_accession if relationship != '=' and genbank_accession == 'na': accession = refseq_accession if written_contigs is not None and (genbank_accession in written_contigs or refseq_accession in written_contigs): logger.info('Accession ' + accession + ' already in the FASTA file, don\'t need to be downloaded') continue new_contig = get_sequence_from_ncbi(accession, fasta_path, eutils_api_key) if new_contig is not None: new_contigs.append(new_contig) return new_contigs @retry(tries=4, delay=2, backoff=1.2, jitter=(1, 3)) def get_sequence_from_ncbi(accession, fasta_path, eutils_api_key): url = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=nuccore&id=' + accession + \ '&rettype=fasta&retmode=text&api_key=' + eutils_api_key + '&tool=eva&email=<EMAIL>' logger.info('Downloading ' + accession) sequence_tmp_path = os.path.dirname(fasta_path) + '/' + accession urllib.request.urlretrieve(url, sequence_tmp_path) with open(sequence_tmp_path) as sequence: first_line = sequence.readline() second_line = sequence.readline() if not second_line: logger.info('FASTA sequence not available for ' + accession) os.remove(sequence_tmp_path) else: contatenate_sequence_to_fasta(fasta_path, sequence_tmp_path) logger.info(accession + " downloaded and added to FASTA sequence") os.remove(sequence_tmp_path) return accession def contatenate_sequence_to_fasta(fasta_path, sequence_path): with open(fasta_path, 'a+') as fasta: with open(sequence_path) as sequence: for line in sequence: # Check that the line is not empty if line.strip(): fasta.write(line) @retry(tries=4, delay=2, backoff=1.2, jitter=(1, 3)) def download_assembly_report(assembly_accession, directory_path): assembly_report_url = get_assembly_report_url(assembly_accession) assembly_report_path = directory_path + os.path.basename(assembly_report_url) os.makedirs(os.path.dirname(assembly_report_path), exist_ok=True) urllib.request.urlretrieve(assembly_report_url, assembly_report_path) urllib.request.urlcleanup() return assembly_report_path def build_output_directory_path(assembly_accession, private_config_args, output_directory): if output_directory is not None: directory_path = output_directory else: eva_root_dir = private_config_args['eva_root_dir'] directory_path = eva_root_dir directory_path += os.path.sep + assembly_accession + os.path.sep logger.info('Files will be downloaded in ' + directory_path) return directory_path def get_args_from_private_config_file(private_config_file): with open(private_config_file) as private_config_file_handle: if 'json' in private_config_file: return json.load(private_config_file_handle) else: if 'yml' in private_config_file: return yaml.safe_load(private_config_file_handle) else: raise TypeError('Configuration file should be either json or yaml') def main(assembly_accession, output_directory, private_config_file, clear): private_config_args = get_args_from_private_config_file(private_config_file) eutils_api_key = private_config_args['eutils_api_key'] directory_path = build_output_directory_path(assembly_accession, private_config_args, output_directory) assembly_report_path = download_assembly_report(assembly_accession, directory_path) build_fasta_from_assembly_report(assembly_report_path, assembly_accession, eutils_api_key, directory_path, clear) if __name__ == "__main__": parser = argparse.ArgumentParser(description='Genome downloader assembly', add_help=False) parser.add_argument("-a", "--assembly-accession", help="Assembly for which the process has to be run, e.g. GCA_000002285.2", required=True) parser.add_argument("-o", "--output-directory", help="Output directory for the assembly report and FASTA file", required=False) parser.add_argument("-p", "--private-config-file", help="Path to the configuration file with private info (JSON format)", required=True) parser.add_argument("-c", "--clear", help="Flag to clear existing data in FASTA file and starting from scratch", action='store_true') parser.add_argument('--help', action='help', help='Show this help message and exit') args = {} try: args = parser.parse_args() main(args.assembly_accession, args.output_directory, args.private_config_file, args.clear) except Exception as ex: logger.exception(ex) sys.exit(1) sys.exit(0)
<reponame>ministryofjustice/moj-product-dashboard # -*- coding: utf-8 -*- from datetime import date from decimal import Decimal from unittest.mock import patch import pytest from ..rate_converter import RateConverter, RATE_TYPES DAY = RATE_TYPES.DAY MONTH = RATE_TYPES.MONTH YEAR = RATE_TYPES.YEAR @pytest.mark.parametrize('on, rate, rate_type, expected', [ (date(2016, 1, 1), '500', DAY, '500'), # Day rate (date(2016, 1, 1), '4600', MONTH, '230'), # Jan / 20 days (date(2016, 2, 1), '4600', MONTH, '219.05'), # Feb / 21 days (date(2016, 3, 1), '4600', MONTH, '219.05'), # Mar / 21 days (date(2016, 4, 1), '4600', MONTH, '219.05'), # Apr / 21 days (date(2016, 5, 1), '4600', MONTH, '230'), # May / 20 days (date(2016, 6, 1), '4600', MONTH, '209.09'), # Jun / 22 days (date(2016, 7, 1), '4600', MONTH, '219.05'), # Jul / 21 days (date(2016, 8, 1), '4600', MONTH, '209.09'), # Aug / 22 days (date(2016, 9, 1), '4600', MONTH, '209.09'), # Sep / 22 days (date(2016, 10, 1), '4600', MONTH, '219.05'), # Oct / 21 days (date(2016, 11, 1), '4600', MONTH, '209.09'), # Nov / 22 days (date(2016, 12, 1), '4600', MONTH, '230'), # Dec / 20 days (date(2014, 1, 1), '60000', YEAR, '237.15'), # 2014 (date(2015, 1, 1), '60000', YEAR, '237.15'), # 2015 (date(2016, 1, 1), '60000', YEAR, '237.15'), # 2016 # use original values (date(2016, 1, 1), '500', DAY.original_value, '500'), (date(2016, 12, 1), '4600', MONTH.original_value, '230'), (date(2016, 1, 1), '60000', YEAR.original_value, '237.15'), (None, '4600', MONTH.original_value, '230'), ]) @patch('dashboard.libs.rate_converter.today') def test_rate_at_certain_time(mock_today, on, rate, rate_type, expected): mock_today.return_value = date(2016, 1, 1) converter = RateConverter(Decimal(rate), rate_type) assert converter.rate_on(on=on) == Decimal(expected) @pytest.mark.parametrize('start_date, end_date, rate, rate_type, expected', [ ((2016, 4, 28), (2016, 4, 28), '500', DAY, '500'), # Day rate ((2016, 1, 1), (2016, 1, 31), '4600', MONTH, '230'), # Jan / 20 days ((2016, 2, 1), (2016, 2, 29), '4600', MONTH, '219.05'), # Feb / 21 days ((2016, 3, 1), (2016, 3, 31), '4600', MONTH, '219.05'), # Mar / 21 days ((2016, 4, 1), (2016, 4, 30), '4600', MONTH, '219.05'), # Apr / 21 days ((2016, 5, 1), (2016, 5, 31), '4600', MONTH, '230'), # May / 20 days ((2016, 6, 1), (2016, 6, 30), '4600', MONTH, '209.09'), # Jun / 22 days ((2016, 7, 1), (2016, 7, 31), '4600', MONTH, '219.05'), # Jul / 21 days ((2016, 8, 1), (2016, 8, 31), '4600', MONTH, '209.09'), # Aug / 22 days ((2016, 9, 1), (2016, 9, 30), '4600', MONTH, '209.09'), # Sep / 22 days ((2016, 10, 1), (2016, 10, 31), '4600', MONTH, '219.05'), # Oct / 21 days ((2016, 11, 1), (2016, 11, 30), '4600', MONTH, '209.09'), # Nov / 22 days ((2016, 12, 1), (2016, 12, 31), '4600', MONTH, '230'), # Dec / 20 days ((2014, 1, 1), (2014, 12, 31), '60000', YEAR, '237.15'), # 2014 ((2015, 1, 1), (2015, 12, 31), '60000', YEAR, '237.15'), # 2015 ((2016, 1, 1), (2016, 12, 31), '60000', YEAR, '237.15'), # 2016 ((2016, 1, 4), (2016, 1, 4), '60000', YEAR, '237.15'), # One day ((2016, 1, 4), (2016, 1, 4), '4600', MONTH, '230'), # One day ((2016, 1, 28), (2016, 2, 2), '4600', MONTH, '224.52'), # Equal days per month ((2016, 1, 28), (2016, 2, 3), '4600', MONTH, '223.43'), # different amount of days in month ((2016, 1, 28), (2016, 6, 3), '4600', MONTH, '219.30'), # multiple months ((2014, 1, 28), (2016, 2, 3), '4600', MONTH, '216.36'), # multiple years ]) def test_cross_month_average(start_date, end_date, rate, rate_type, expected): converter = RateConverter(Decimal(rate), rate_type) assert converter.rate_between( start_date=date(*start_date), end_date=date(*end_date)) == Decimal(expected)
#!/usr/bin/python -u # -*- coding: UTF-8 -*- # pylint: disable=C0111 import subprocess import unittest import binascii from binascii import a2b_base64 from base64 import standard_b64encode from pyxmpp2 import sasl from pyxmpp2.sasl.core import CLIENT_MECHANISMS_D from pyxmpp2.test import _support import logging logger = logging.getLogger("pyxmpp2.tests.sasl_gsasl") COMPATIBLE_GSASL = [ b"gsasl (GNU SASL) 1.6.1", b"gsasl (GNU SASL) 1.6.0", ] gsasl_available = False # pylint: disable=C0103 gsasl_client_mechanisms = [] # pylint: disable=C0103 gsasl_server_mechanisms = [] # pylint: disable=C0103 def check_gsasl(): # pylint: disable=W0603,E1103 global gsasl_available global gsasl_client_mechanisms global gsasl_server_mechanisms try: pipe = subprocess.Popen(["gsasl", "--version"], stdout = subprocess.PIPE) stdout = pipe.communicate()[0] code = pipe.wait() except OSError: code = -1 if code: return version = stdout.split(b"\n", 1)[0].strip() if version not in COMPATIBLE_GSASL: logger.debug("GSASL version '{0}' not known to be compatible" .format(version)) return if logger.isEnabledFor(logging.DEBUG): quiet = [] else: quiet = ["--quiet"] try: pipe = subprocess.Popen(["gsasl", "--client-mechanisms"] + quiet, stdout = subprocess.PIPE, stdin = subprocess.PIPE) stdout = pipe.communicate(b"NjY=\n")[0] stdout = stdout.strip().rsplit(b"\n", 1)[-1] code = pipe.wait() except OSError: code = -1 if code: return gsasl_available = True gsasl_client_mechanisms = [s for s in stdout.decode("us-ascii").split()] logger.debug("client mechanisms: {0!r}".format(gsasl_client_mechanisms)) try: pipe = subprocess.Popen(["gsasl", "--server-mechanisms"] + quiet, stdout = subprocess.PIPE, stdin = subprocess.PIPE) stdout = pipe.communicate(b"abcd\n" * 4)[0] stdout = stdout.strip().rsplit(b"\n", 1)[-1] code = pipe.wait() except OSError: code = -1 if not code: gsasl_server_mechanisms = [s for s in stdout.decode("us-ascii").split()] logger.debug("server mechanisms: {0!r}".format(gsasl_server_mechanisms)) class PasswordDatabase(sasl.PasswordDatabase): def __init__(self, username, password, realms = None): self.username = username self.password = password if realms: self.realms = realms else: self.realms = [] def get_password(self, username, acceptable_formats, properties): if self.username == username: return self.password, "plain" else: return None, None class GSASLError(Exception): pass class OurSASLError(Exception): pass @unittest.skipIf("gsasl" not in _support.RESOURCES, "GSASL usage disabled") class TestSASLClientvsGSASL(unittest.TestCase): @classmethod def setUpClass(cls): if not gsasl_available: raise unittest.SkipTest("GSASL utility not available") def test_PLAIN_good_pass_no_authzid(self): if "PLAIN" not in gsasl_server_mechanisms: raise unittest.SkipTest( "GSASL has no PLAIN support") authenticator = sasl.client_authenticator_factory("PLAIN") auth_prop = { "username": u"username", "password": u"<PASSWORD>", } ok, props = self.try_with_gsasl("PLAIN", authenticator, auth_prop) self.assertTrue(ok) self.assertFalse(props.get("authzid")) def test_PLAIN_good_pass_authzid(self): if "PLAIN" not in gsasl_server_mechanisms: raise unittest.SkipTest( "GSASL has no PLAIN support") authenticator = sasl.client_authenticator_factory("PLAIN") auth_prop = { "username": u"username", "password": u"<PASSWORD>", "authzid": u"zid", } ok, props = self.try_with_gsasl("PLAIN", authenticator, auth_prop) self.assertTrue(ok) self.assertEqual(props.get("authzid"), "zid") def test_PLAIN_bad_pass_no_authzid(self): if "PLAIN" not in gsasl_server_mechanisms: raise unittest.SkipTest( "GSASL has no PLAIN support") authenticator = sasl.client_authenticator_factory("PLAIN") auth_prop = { "username": u"username", "password": u"<PASSWORD>", } ok, props = self.try_with_gsasl("PLAIN", authenticator, auth_prop) self.assertFalse(ok) self.assertFalse(props.get("authzid")) def test_DIGEST_MD5_good_pass_no_authzid(self): if "DIGEST-MD5" not in gsasl_server_mechanisms: raise unittest.SkipTest( "GSASL has no DIGEST-MD5 support") authenticator = sasl.client_authenticator_factory("DIGEST-MD5") auth_prop = { "username": u"username", "password": u"<PASSWORD>", "service-type": u"xmpp", "service-domain": u"pyxmpp.jajcus.net", "service-hostname": u"test.pyxmpp.jajcus.net", } ok, props = self.try_with_gsasl("DIGEST-MD5", authenticator, auth_prop, ["--service=xmpp", "--realm=jajcus.net", "--host=test.pyxmpp.jajcus.net", "--service-name=pyxmpp.jajcus.net"]) self.assertTrue(ok) self.assertFalse(props.get("authzid")) def test_DIGEST_MD5_good_pass_authzid(self): if "DIGEST-MD5" not in gsasl_server_mechanisms: raise unittest.SkipTest( "GSASL has no DIGEST-MD5 support") authenticator = sasl.client_authenticator_factory("DIGEST-MD5") auth_prop = { "username": u"username", "password": u"<PASSWORD>", "service-type": u"xmpp", "service-domain": u"pyxmpp.jajcus.net", "service-hostname": u"test.pyxmpp.jajcus.net", "authzid": u"zid", } ok, props = self.try_with_gsasl("DIGEST-MD5", authenticator, auth_prop, ["--service=xmpp", "--realm=jajcus.net", "--host=test.pyxmpp.jajcus.net", "--service-name=pyxmpp.jajcus.net"]) self.assertTrue(ok) self.assertEqual(props.get("authzid"), u"zid") def test_DIGEST_MD5_bad_pass_no_authzid(self): if "DIGEST-MD5" not in gsasl_server_mechanisms: raise unittest.SkipTest( "GSASL has no DIGEST-MD5 support") authenticator = sasl.client_authenticator_factory("DIGEST-MD5") auth_prop = { "username": u"username", "password": u"<PASSWORD>", "service-type": u"xmpp", "service-domain": u"pyxmpp.jajcus.net", "service-hostname": u"test.pyxmpp.jajcus.net", } ok, dummy = self.try_with_gsasl("DIGEST-MD5", authenticator, auth_prop, ["--service=xmpp", "--realm=jajcus.net", "--host=test.pyxmpp.jajcus.net", "--service-name=pyxmpp.jajcus.net"]) self.assertFalse(ok) def test_SCRAM_SHA_1_good_pass_no_authzid(self): if "SCRAM-SHA-1" not in gsasl_server_mechanisms: raise unittest.SkipTest( "GSASL has no SCRAM-SHA-1 support") authenticator = sasl.client_authenticator_factory("SCRAM-SHA-1") auth_prop = { "username": u"username", "password": u"<PASSWORD>", } ok, props = self.try_with_gsasl("SCRAM-SHA-1", authenticator, auth_prop, ["--no-cb"]) self.assertTrue(ok) self.assertFalse(props.get("authzid")) def test_SCRAM_SHA_1_good_pass_authzid(self): if "SCRAM-SHA-1" not in gsasl_server_mechanisms: raise unittest.SkipTest( "GSASL has no SCRAM-SHA-1 support") authenticator = sasl.client_authenticator_factory("SCRAM-SHA-1") auth_prop = { "username": u"username", "password": u"<PASSWORD>", "authzid": u"zid", } ok, props = self.try_with_gsasl("SCRAM-SHA-1", authenticator, auth_prop, ["--no-cb"]) self.assertTrue(ok) self.assertEqual(props.get("authzid"), "zid") def test_SCRAM_SHA_1_quoting(self): if "SCRAM-SHA-1" not in gsasl_server_mechanisms: raise unittest.SkipTest( "GSASL has no SCRAM-SHA-1 support") authenticator = sasl.client_authenticator_factory("SCRAM-SHA-1") auth_prop = { "username": u"pi=3,14", "password": u"<PASSWORD>", "authzid": u"e=2,72", } ok, props = self.try_with_gsasl("SCRAM-SHA-1", authenticator, auth_prop, ["--no-cb"], username = "pi=3,14") self.assertTrue(ok) self.assertEqual(props.get("authzid"), "e=2,72") def test_SCRAM_SHA_1_bad_pass_no_authzid(self): if "SCRAM-SHA-1" not in gsasl_server_mechanisms: raise unittest.SkipTest( "GSASL has no SCRAM-SHA-1 support") authenticator = sasl.client_authenticator_factory("SCRAM-SHA-1") auth_prop = { "username": u"username", "password": u"<PASSWORD>", } ok, dummy = self.try_with_gsasl("SCRAM-SHA-1", authenticator, auth_prop, ["--no-cb"]) self.assertFalse(ok) def test_SCRAM_SHA_1_good_pass_downgrade(self): # Check protection from channel-binding downgrade. if "SCRAM-SHA-1-PLUS" not in gsasl_server_mechanisms: raise unittest.SkipTest( "GSASL has no SCRAM-SHA-1 support") authenticator = sasl.client_authenticator_factory("SCRAM-SHA-1") auth_prop = { "enabled_mechanisms": ["SCRAM-SHA-1", "SCRAM-SHA-1-PLUS"], "username": u"username", "password": u"<PASSWORD>", } cb_data = b"<PASSWORD>3456789ab" ok, dummy = self.try_with_gsasl("SCRAM-SHA-1", authenticator, auth_prop, extra_data = standard_b64encode(cb_data)) self.assertFalse(ok) @unittest.skipIf("SCRAM-SHA-1-PLUS" not in CLIENT_MECHANISMS_D, "SCRAM-SHA-1-PLUS not available in PyXMPP2") def test_SCRAM_SHA_1_PLUS_good_pw_good_cb(self): if "SCRAM-SHA-1-PLUS" not in gsasl_server_mechanisms: raise unittest.SkipTest( "GSASL has no SCRAM-SHA-1-PLUS support") authenticator = sasl.client_authenticator_factory("SCRAM-SHA-1-PLUS") cb_data = b"0123456789ab" auth_prop = { "username": u"username", "password": u"<PASSWORD>", "channel-binding": { "tls-unique": cb_data, }, } ok, props = self.try_with_gsasl("SCRAM-SHA-1-PLUS", authenticator, auth_prop, extra_data = standard_b64encode(cb_data)) self.assertTrue(ok) self.assertFalse(props.get("authzid")) @unittest.skipIf("SCRAM-SHA-1-PLUS" not in CLIENT_MECHANISMS_D, "SCRAM-SHA-1-PLUS not available in PyXMPP2") def test_SCRAM_SHA_1_PLUS_bad_pw_good_cb(self): if "SCRAM-SHA-1-PLUS" not in gsasl_server_mechanisms: raise unittest.SkipTest( "GSASL has no SCRAM-SHA-1-PLUS support") authenticator = sasl.client_authenticator_factory("SCRAM-SHA-1-PLUS") cb_data = b"0123456789ab" auth_prop = { "username": u"username", "password": u"<PASSWORD>", "channel-binding": { "tls-unique": cb_data, }, } ok, dummy = self.try_with_gsasl("SCRAM-SHA-1-PLUS", authenticator, auth_prop, extra_data = standard_b64encode(cb_data)) self.assertFalse(ok) @unittest.skipIf("SCRAM-SHA-1-PLUS" not in CLIENT_MECHANISMS_D, "SCRAM-SHA-1-PLUS not available in PyXMPP2") def test_SCRAM_SHA_1_PLUS_good_pw_bad_cb(self): if "SCRAM-SHA-1-PLUS" not in gsasl_server_mechanisms: raise unittest.SkipTest( "GSASL has no SCRAM-SHA-1-PLUS support") authenticator = sasl.client_authenticator_factory("SCRAM-SHA-1-PLUS") cb_data = b"0123456789ab" auth_prop = { "username": u"username", "password": u"<PASSWORD>", "channel-binding": { "tls-unique": cb_data, }, } cb_data = b"BAD_BAD_BAD_" ok, dummy = self.try_with_gsasl("SCRAM-SHA-1-PLUS", authenticator, auth_prop, extra_data = standard_b64encode(cb_data)) self.assertFalse(ok) @staticmethod def try_with_gsasl(mechanism, authenticator, auth_properties, gsasl_args = [], extra_data = None, username = "username"): # pylint: disable=W0102,R0914,R0912,R0915 cmd = ["gsasl", "--server", "--mechanism=" + mechanism, "--password=<PASSWORD>", "--authentication-id={0}".format(username)] + gsasl_args if logger.isEnabledFor(logging.DEBUG): stderr = None logger.debug("cmd: %r", " ".join(cmd)) else: cmd.append("--quiet") stderr = open("/dev/null", "w") pipe = subprocess.Popen(cmd, bufsize = 1, stdout = subprocess.PIPE, stdin = subprocess.PIPE, stderr = stderr) if stderr: stderr.close() if extra_data: data = extra_data + b"\n" logger.debug("OUT: %r", data) pipe.stdin.write(data) pipe.stdin.flush() mech = pipe.stdout.readline() logger.debug("IN: %r", mech) if extra_data and extra_data in mech: mech = pipe.stdout.readline() logger.debug("IN: %r", mech) mech = mech.strip().decode("utf-8") if mech != mechanism: pipe.stdin.close() pipe.stdout.close() raise GSASLError, "GSASL returned different mechanism: " + mech result = authenticator.start(auth_properties) if isinstance(result, sasl.Failure): pipe.stdin.close() pipe.stdout.close() raise OurSASLError, result.reason response = result.encode() if response: data = (response + "\n").encode("utf-8") logger.debug("OUT: %r", data) pipe.stdin.write(data) pipe.stdin.flush() ignore_empty_challenge = True else: ignore_empty_challenge = False while True: challenge = pipe.stdout.readline().strip() if not challenge: if ignore_empty_challenge: logger.debug("Ignoring empty initial challenge") ignore_empty_challenge = False continue else: break if challenge.startswith(b'Mechanism requested'): continue try: decoded = a2b_base64(challenge) except (ValueError, binascii.Error): logger.debug("not base64: %r", challenge) if challenge.startswith(b'\x1b['): logger.debug("echo: %r", challenge) # for some unknown reason gsasl echoes our data back response = None continue if response and a2b_base64(response.encode("utf-8")) == decoded: logger.debug("echo: %r", challenge) # for some unknown reason gsasl echoes our data back response = None continue logger.debug("IN: %r", challenge) result = authenticator.challenge(decoded) if isinstance(result, sasl.Failure): raise OurSASLError, result.reason response = result.encode() data = (response + "\n").encode("utf-8") logger.debug("OUT: %r", data) pipe.stdin.write(data) pipe.stdin.flush() if not response: break pipe.stdin.close() pipe.stdout.close() code = pipe.wait() if code: return False, {} result = authenticator.finish(None) if isinstance(result, sasl.Success): return True, result.properties else: return False, {} @unittest.skipIf("gsasl" not in _support.RESOURCES, "GSASL usage disabled") class TestSASLServervsGSASL(unittest.TestCase): @classmethod def setUpClass(cls): if not gsasl_available: raise unittest.SkipTest("GSASL utility not available") def test_PLAIN_good_pass_no_authzid(self): if "PLAIN" not in gsasl_client_mechanisms: raise unittest.SkipTest("GSASL has no PLAIN support") pwdb = PasswordDatabase("username", "good") authenticator = sasl.server_authenticator_factory("PLAIN", pwdb) ok, props = self.try_with_gsasl("PLAIN", authenticator, {}) self.assertTrue(ok) self.assertFalse(props.get("authzid")) def test_PLAIN_bad_pass_no_authzid(self): if "PLAIN" not in gsasl_client_mechanisms: raise unittest.SkipTest("GSASL has no PLAIN support") pwdb = PasswordDatabase("username", "bad") authenticator = sasl.server_authenticator_factory("PLAIN", pwdb) with self.assertRaises(OurSASLError) as err: self.try_with_gsasl("PLAIN", authenticator, {}) self.assertEqual(err.exception.args[0], "not-authorized") def test_DIGEST_MD5_good_pass_no_authzid(self): if "DIGEST-MD5" not in gsasl_client_mechanisms: raise unittest.SkipTest( "GSASL has no DIGEST-MD5 support") pwdb = PasswordDatabase("username", "good") authenticator = sasl.server_authenticator_factory("DIGEST-MD5", pwdb) auth_prop = { "service-type": u"xmpp", "service-domain": u"pyxmpp.jajcus.net", "service-hostname": u"test.pyxmpp.jajcus.net", } ok, props = self.try_with_gsasl("DIGEST-MD5", authenticator, auth_prop, [ "--service=xmpp", "--realm=jajcus.net", "--host=test.pyxmpp.jajcus.net", "--service-name=pyxmpp.jajcus.net", "--quality-of-protection=qop-auth"]) self.assertTrue(ok) self.assertIsNone(props.get("authzid")) def test_DIGEST_MD5_good_pass_authzid(self): if "DIGEST-MD5" not in gsasl_client_mechanisms: raise unittest.SkipTest( "GSASL has no DIGEST-MD5 support") pwdb = PasswordDatabase("username", "good") authenticator = sasl.server_authenticator_factory("DIGEST-MD5", pwdb) auth_prop = { "service-type": u"xmpp", "service-domain": u"pyxmpp.jajcus.net", "service-hostname": u"test.pyxmpp.jajcus.net", } ok, props = self.try_with_gsasl("DIGEST-MD5", authenticator, auth_prop, [ "--service=xmpp", "--realm=jajcus.net", "--host=test.pyxmpp.jajcus.net", "--service-name=pyxmpp.jajcus.net", "--quality-of-protection=qop-auth", "--authorization-id=zid"]) self.assertTrue(ok) self.assertEqual(props.get("authzid"), "zid") def test_DIGEST_MD5_bad_pass_no_authzid(self): if "DIGEST-MD5" not in gsasl_client_mechanisms: raise unittest.SkipTest( "GSASL has no DIGEST-MD5 support") pwdb = PasswordDatabase("username", "bad") authenticator = sasl.server_authenticator_factory("DIGEST-MD5", pwdb) auth_prop = { "service-type": u"xmpp", "service-domain": u"pyxmpp.jajcus.net", "service-hostname": u"test.pyxmpp.jajcus.net", } with self.assertRaises(OurSASLError) as err: self.try_with_gsasl("DIGEST-MD5", authenticator, auth_prop, [ "--service=xmpp", "--realm=jajcus.net", "--host=test.pyxmpp.jajcus.net", "--service-name=pyxmpp.jajcus.net", "--quality-of-protection=qop-auth"]) self.assertEqual(err.exception.args[0], "not-authorized") def test_SCRAM_SHA_1_good_pass_no_authzid(self): if "SCRAM-SHA-1" not in gsasl_client_mechanisms: raise unittest.SkipTest( "GSASL has no SCRAM-SHA-1 support") pwdb = PasswordDatabase("username", "good") authenticator = sasl.server_authenticator_factory("SCRAM-SHA-1", pwdb) auth_prop = { "enabled_mechanisms": ["SCRAM-SHA-1"]} ok, props = self.try_with_gsasl("SCRAM-SHA-1", authenticator, auth_prop, [ "--no-cb"]) self.assertTrue(ok) self.assertIsNone(props.get("authzid")) def test_SCRAM_SHA_1_good_pass_authzid(self): if "SCRAM-SHA-1" not in gsasl_client_mechanisms: raise unittest.SkipTest( "GSASL has no SCRAM-SHA-1 support") pwdb = PasswordDatabase("username", "good") authenticator = sasl.server_authenticator_factory("SCRAM-SHA-1", pwdb) auth_prop = { } ok, props = self.try_with_gsasl("SCRAM-SHA-1", authenticator, auth_prop, [ "--no-cb", "--authorization-id=zid"]) self.assertTrue(ok) self.assertEqual(props.get("authzid"), "zid") def test_SCRAM_SHA_1_quoting(self): if "SCRAM-SHA-1" not in gsasl_client_mechanisms: raise unittest.SkipTest( "GSASL has no SCRAM-SHA-1 support") pwdb = PasswordDatabase("pi=3,14", "good") authenticator = sasl.server_authenticator_factory("SCRAM-SHA-1", pwdb) auth_prop = { } ok, props = self.try_with_gsasl("SCRAM-SHA-1", authenticator, auth_prop, [ "--no-cb", "--authorization-id=e=2,72"], username = "pi=3,14") self.assertTrue(ok) self.assertEqual(props.get("authzid"), "e=2,72") def test_SCRAM_SHA_1_bad_username(self): if "SCRAM-SHA-1" not in gsasl_client_mechanisms: raise unittest.SkipTest( "GSASL has no SCRAM-SHA-1 support") pwdb = PasswordDatabase("bad", "good") authenticator = sasl.server_authenticator_factory("SCRAM-SHA-1", pwdb) auth_prop = { } with self.assertRaises(OurSASLError) as err: self.try_with_gsasl("SCRAM-SHA-1", authenticator, auth_prop, [ "--no-cb"]) self.assertEqual(err.exception.args[0], "not-authorized") def test_SCRAM_SHA_1_bad_pass_no_authzid(self): if "SCRAM-SHA-1" not in gsasl_client_mechanisms: raise unittest.SkipTest( "GSASL has no SCRAM-SHA-1 support") pwdb = PasswordDatabase("username", "bad") authenticator = sasl.server_authenticator_factory("SCRAM-SHA-1", pwdb) auth_prop = { } with self.assertRaises(OurSASLError) as err: self.try_with_gsasl("SCRAM-SHA-1", authenticator, auth_prop, [ "--no-cb"]) self.assertEqual(err.exception.args[0], "not-authorized") def test_SCRAM_SHA_1_good_pass_downgrade(self): if "SCRAM-SHA-1" not in gsasl_client_mechanisms: raise unittest.SkipTest( "GSASL has no SCRAM-SHA-1 support") pwdb = PasswordDatabase("username", "good") authenticator = sasl.server_authenticator_factory("SCRAM-SHA-1", pwdb) auth_prop = { "enabled_mechanisms": ["SCRAM-SHA-1", "SCRAM-SHA-1-PLUS"]} cb_data = b"0123456789ab" with self.assertRaises(OurSASLError) as err: self.try_with_gsasl("SCRAM-SHA-1", authenticator, auth_prop, extra_data = standard_b64encode(cb_data)) self.assertEqual(err.exception.args[0], "not-authorized") def test_SCRAM_SHA_1_PLUS_good_pass_authzid(self): if "SCRAM-SHA-1-PLUS" not in gsasl_client_mechanisms: raise unittest.SkipTest( "GSASL has no SCRAM-SHA-1-PLUS support") pwdb = PasswordDatabase("username", "good") authenticator = sasl.server_authenticator_factory("SCRAM-SHA-1-PLUS", pwdb) cb_data = b"0123456789ab" auth_prop = { "channel-binding": {"tls-unique": cb_data} } ok, props = self.try_with_gsasl("SCRAM-SHA-1-PLUS", authenticator, auth_prop, ["--authorization-id=zid"], extra_data = standard_b64encode(cb_data)) self.assertTrue(ok) self.assertEqual(props.get("authzid"), "zid") def test_SCRAM_SHA_1_PLUS_bad_pass_no_authzid(self): if "SCRAM-SHA-1-PLUS" not in gsasl_client_mechanisms: raise unittest.SkipTest( "GSASL has no SCRAM-SHA-1-PLUS support") pwdb = PasswordDatabase("username", "bad") authenticator = sasl.server_authenticator_factory("SCRAM-SHA-1-PLUS", pwdb) cb_data = b"0123456789ab" auth_prop = { "channel-binding": {"tls-unique": cb_data} } with self.assertRaises(OurSASLError) as err: self.try_with_gsasl("SCRAM-SHA-1-PLUS", authenticator, auth_prop, extra_data = standard_b64encode(cb_data)) self.assertEqual(err.exception.args[0], "not-authorized") def test_SCRAM_SHA_1_PLUS_good_pass_bad_cb(self): if "SCRAM-SHA-1-PLUS" not in gsasl_client_mechanisms: raise unittest.SkipTest( "GSASL has no SCRAM-SHA-1-PLUS support") pwdb = PasswordDatabase("username", "good") authenticator = sasl.server_authenticator_factory("SCRAM-SHA-1-PLUS", pwdb) cb_data = b"0123456789ab" auth_prop = { "channel-binding": {"tls-unique": cb_data} } bad_cb_data = b"ab0123456789" with self.assertRaises(OurSASLError) as err: self.try_with_gsasl("SCRAM-SHA-1-PLUS", authenticator, auth_prop, extra_data = standard_b64encode(bad_cb_data)) self.assertEqual(err.exception.args[0], "not-authorized") @staticmethod def try_with_gsasl(mechanism, authenticator, auth_prop, gsasl_args = [], extra_data = None, username = "username"): # pylint: disable=W0102,R0914,R0912,R0915 cmd = ["gsasl", "--client", "--mechanism=" + mechanism, "--password=<PASSWORD>", "--authentication-id={0}".format(username)] + gsasl_args if logger.isEnabledFor(logging.DEBUG): stderr = None logger.debug("cmd: %r", " ".join(cmd)) else: cmd.append("--quiet") stderr = open("/dev/null", "w") pipe = subprocess.Popen(cmd, bufsize = 1, stdout = subprocess.PIPE, stdin = subprocess.PIPE, stderr = stderr) if stderr: stderr.close() if extra_data: data = extra_data + b"\n" logger.debug("OUT: %r", data) pipe.stdin.write(data) pipe.stdin.flush() mech = pipe.stdout.readline() logger.debug("IN: %r", mech) if extra_data and extra_data in mech: mech = pipe.stdout.readline() logger.debug("IN: %r", mech) mech = mech.strip().decode("utf-8") logger.debug("IN: %r", mech) if mech != mechanism: raise GSASLError, "GSASL returned different mechanism: " + mech data = pipe.stdout.readline().strip() if data: result = authenticator.start(auth_prop, a2b_base64(data)) else: result = authenticator.start(auth_prop, None) if isinstance(result, sasl.Failure): pipe.stdin.close() pipe.stdout.close() raise OurSASLError, result.reason if isinstance(result, sasl.Success): pipe.stdin.close() pipe.stdout.close() code = pipe.wait() if code: raise GSASLError, "GSASL exited with {0}".format(code) return True, result.properties challenge = result.encode() data = (challenge + "\n").encode("utf-8") logger.debug("OUT: %r", data) pipe.stdin.write(data) pipe.stdin.flush() success = None while not success: response = pipe.stdout.readline().strip() if not response: break if response.startswith(b'Mechanism requested'): continue try: decoded = a2b_base64(response) except (ValueError, binascii.Error): logger.debug("not base64: %r", response) if response.startswith(b'\x1b[') or challenge and ( a2b_base64(challenge.encode("utf-8")) == decoded): logger.debug("echo: %r", challenge) # for some unknown reason gsasl echoes our data back challenge = None continue logger.debug("IN: %r", response) result = authenticator.response(decoded) if isinstance(result, sasl.Failure): pipe.stdin.close() pipe.stdout.close() raise OurSASLError, result.reason if isinstance(result, sasl.Success): success = result if not success.data: break challenge = result.encode() data = (challenge + "\n").encode("utf-8") logger.debug("OUT: %r", data) pipe.stdin.write(data) pipe.stdin.flush() pipe.communicate() pipe.stdin.close() pipe.stdout.close() code = pipe.wait() if code: raise GSASLError, "GSASL exited with {0}".format(code) if success: return True, success.properties else: return False, None # pylint: disable=W0611 from pyxmpp2.test._support import load_tests, setup_logging def setUpModule(): check_gsasl() setup_logging() if __name__ == "__main__": unittest.main()
import numpy as np from mmd.molecule import * from mmd.realtime import * from mmd.utils.spectrum import * import matplotlib.pyplot as plt from tqdm import tqdm import matplotlib.colors as colors import scipy.interpolate import matplotlib as mpl import matplotlib.gridspec as gridspec import sys h2 = """ 0 1 H 0.52 0.0 0.52 H 0.0 0.0 0.0 """ # init molecule and build integrals mol = Molecule(geometry=h2,basis='6-31ppg') mol.build() # do the SCF mol.RHF() # Make realtime N = 2000 dt = 0.10 Emax = 0.0001 def mypulse(t): pulse = 0.0 pulse += np.exp(-(t)**2/dt) pulse += np.exp(-(t-tau)**2/dt) return pulse def delta(t): pulse = 0.0 pulse += np.exp(-(t)**2/dt) return pulse taus = np.arange(15,20,0.8) specs1 = [] specs2 = [] start = int(max(taus)/dt) + 1 # Get the reference signal (e.g. both pulses at same time) tau = 0.0 rt = RealTime(mol,numsteps=N,stepsize=dt,field=Emax,pulse=mypulse) rt.Magnus2(direction='z') refZ,frequency = pade(rt.time[start:],rt.dipole[start:]-rt.dipole[0]) refsignal = np.real(refZ*refZ.conjugate()) # Do a few sample delta pulses and plot cmap1 = mpl.cm.tab20 cmap2 = mpl.cm.tab20 for idx, tau in enumerate([15,17,20]): rt = RealTime(mol,numsteps=N,stepsize=dt,field=Emax,pulse=mypulse) rt.Magnus2(direction='z') #plt.plot(rt.time*0.0241888425,np.array(rt.shape)*rt.field,color=cmap1(idx/3.0)) ax = plt.gca() plt.plot(rt.time*0.0241888425,-1*np.array(rt.dipole),color=cmap2(2*idx)) ax.fill_between(rt.time*0.0241888425, np.array(rt.shape)*rt.field, 0, interpolate=True, color=cmap1(2*idx+1), alpha=0.8,label=str('{0:.2f}'.format(tau*0.024))) tau = 0.0 rt = RealTime(mol,numsteps=N,stepsize=dt,field=Emax,pulse=delta) rt.Magnus2(direction='z') plt.plot(rt.time*0.0241888425,-1*np.array(rt.dipole),color='black',ls='--',label='Reference') ax.fill_between(rt.time*0.0241888425, np.array(np.exp(-(rt.time)**2/dt))*rt.field, 0, interpolate=True, color='black') plt.legend(title='Pulse delay / fs') plt.xlim([0,0.80]) plt.xlabel('Time / fs') plt.ylabel('Induced z-Dipole / arb. units') plt.yticks([]) plt.title('Induced dipole as function of pulse delay, H$_2$ 6-31++G') plt.savefig('pulses.pdf',bbox_inches='tight') plt.close() # Now do the pulses with delays 'tau' cmap = mpl.cm.cividis for i,tau in enumerate(tqdm(taus)): rt = RealTime(mol,numsteps=N,stepsize=dt,field=Emax,pulse=mypulse) rt.Magnus2(direction='z') signalz,frequency = pade(rt.time[start:],rt.dipole[start:]-rt.dipole[0]) signal = np.real(signalz*signalz.conjugate()) specs2.append((signal-refsignal)*frequency/np.linalg.norm(signal - refsignal)) plt.plot(frequency*27.2114,frequency*(signal-refsignal),color=cmap(i/float(len(taus))),label=str('{0:.2f}'.format(tau*0.024))) plt.xlabel('Energy (eV)',fontsize=18) plt.xticks(fontsize=14) plt.yticks([]) plt.xlim([min(27.2114*frequency),max(27.2114*frequency)]) plt.legend(title='Pulse delay / fs',loc=2) #plt.title('$\Delta$ Absorption, H$_2$ 6-31++G') plt.savefig('delta-absorption.pdf',bbox_inches='tight') plt.close() S = np.vstack(specs2) # collect time series into matrix N = np.zeros((S.shape[0],S.shape[0])) for j in range(len(N)): for k in range(len(N)): if j == k: N[j,k] = 0 else: N[j,k] = 1./(np.pi*(k-j)) #cov = np.cov(S,y=np.dot(N,S),rowvar=False,bias=False) # create synchr. correlation matrix norm = 1./(S.shape[0] - 1) cov = norm*np.dot(S.T,S) acov = norm*np.dot(S.T,np.dot(N,S)) frequency *= 27.2114 spread = min([abs(cov.min()),abs(cov.max())])*0.7 levels = np.linspace(-spread,spread,10) fig = plt.figure(1) #fig.suptitle('Asynchronous correlation spectrum',size=14) n = 6 # defines relative aspect between spectra and correlation plot gridspec.GridSpec(n,n) # large correlation plot plt.subplot2grid((n,n), (1,1), colspan=(n-1),rowspan=(n-1)) plt.tick_params(axis='y', labelright='on',right='on') plt.xlabel('Energy (eV)',size=18) plt.ylabel('Energy (eV)',size=18) ax = plt.gca() # get current axis object ax.yaxis.set_label_position('right') ax.set_xticks([10,12,14,16,18,20]) ax.set_yticks([10,12,14,16,18,20]) contours = plt.contourf(cov,extent=[frequency.min(),frequency.max(),frequency.min(),frequency.max()],levels=levels,extend='both',cmap='coolwarm') plt.plot(frequency,frequency,lw=0.8,ls=':',color='black') plt.xticks(fontsize=14) # top spectrum plt.subplot2grid((n,n),(0,1), colspan=(n-1)) plt.xticks([]) plt.yticks([]) plt.plot(frequency,refsignal,color='black') plt.xlim([min(frequency),max(frequency)]) # left spectrum plt.subplot2grid((n,n),(1,0), rowspan=(n-1)) plt.xticks([]) plt.yticks([]) plt.plot(-refsignal,frequency,color='black') plt.ylim([min(frequency),max(frequency)]) plt.locator_params(axis='y', nbins=5) plt.locator_params(axis='x', nbins=5) fig.subplots_adjust(hspace=0,wspace=0) #cbar = plt.colorbar() #cbar.ax.set_yticklabels([]) #cbar.set_ticks([]) #cbar = plt.colorbar(format='%.0e') #cbar.ax.set_ylabel('Correlation / arb. units') fig.set_size_inches(w=5,h=5)#plt.show() plt.savefig('correlation.pdf', bbox_inches='tight')
# -*- coding:utf-8 -*- import numpy as np from tqdm import tqdm import matplotlib.pyplot as plt def find_next_state(current_state): # to select randomly the action to next state if np.random.binomial(1, 0.5): current_state += 1 else: current_state -= 1 return current_state class Random_Walk_Batch_Update(object): def __init__(self, initial_state=3, num_state=7, step_size=0.001): self.initial_state = initial_state # start in the center state, C, state number is 3 self.num_state = num_state # the number of states self.step_size = step_size # the step_size is the step size self.right_action = 1 # proceed right self.left_action = 0 # proceed left self.states = np.arange(num_state) # the set/array of states self.initial_value = np.ones(num_state) / 2 # the initial values of all states self.initial_value[0] = 0 self.initial_value[-1] = 1 self.real_value = np.arange(num_state) / (num_state - 1) # the true values of all states def temporal_difference(self): """ the temporal difference method :return: """ state = self.initial_state trajectory = [state] rewards = [] while True: next_state = find_next_state(state) # to find the next state reward = 0 # if next_state == self.states[-1]: # reward = 1 rewards.append(reward) trajectory.append(next_state) # value[state] += self.step_size * (reward + value[next_state] - value[state]) # TD update equation if next_state == 0 or next_state == self.states[-1]: break state = next_state return trajectory, rewards def monte_carlo(self): """ the Monte Carlo method :return: """ state = self.initial_state trajectory = [state] while True: next_state = find_next_state(state) trajectory.append(next_state) # ending up with left terminal state,that state is 0,all returns are 0 if next_state == 0: returns = 0 break # ending up with right terminal state, that state is 6, all returns are 1 if next_state == self.states[-1]: returns = 1 break state = next_state # for k in trajectory[:-1]: # value[k] += self.step_size * (returns - value[k]) return trajectory, [returns] * (len(trajectory) - 1) def batch_update_rsm(self, episodes=100, method_flag=False): """ to compute the root mean-squared error of batch updating :param episodes: the number of episodes :param method_flag: whether use MC method or TD method, default method is TD :return: """ error_rms = np.zeros(episodes) current_value = self.initial_value.copy() trajectory = [] rewards = [] for idx in range(episodes): # Monte Carlo method if True if method_flag: list_trajectory, list_rewards = self.monte_carlo() # default method is temporal-difference else: list_trajectory, list_rewards = self.temporal_difference() trajectory.append(list_trajectory) rewards.append(list_rewards) while True: temp_array = np.zeros(self.num_state) for track, reward in zip(trajectory, rewards): for i in range(len(track) - 1): if method_flag: # Monte Carlo method if True temp_array[track[i]] += reward[i] - current_value[track[i]] else: # default method is temporal-difference temp_array[track[i]] += reward[i] + current_value[track[i+1]] - \ current_value[track[i]] temp_array *= self.step_size current_value += temp_array # batch updating for state value if np.sum(np.abs(temp_array)) < 1e-3: break error_rms[idx] += np.sqrt(np.linalg.norm(current_value-self.real_value)**2 / len(current_value[1:-1])) return error_rms if __name__ == "__main__": runs = 100 # the number of runs epi = 100 # the number of episodes random_walk_batch_update = Random_Walk_Batch_Update() td_error_rsm = 0 mc_error_rsm = 0 for run in tqdm(range(runs)): td_error_rsm += random_walk_batch_update.batch_update_rsm(episodes=epi, method_flag=False) mc_error_rsm += random_walk_batch_update.batch_update_rsm(episodes=epi, method_flag=True) plt.plot(td_error_rsm/runs, label=r"TD", color="b") plt.plot(mc_error_rsm/runs, label=r"MC", color="r") plt.xlim([0, 100]) plt.legend() plt.savefig("./images/Figure6-2.png") plt.show() plt.close() print("Completed!!! You can check it in the 'images' directory!")
<gh_stars>1-10 import random import unittest from src.common.train_test_split import train_test_split_list from src.common.train_test_split import train_test_split_gt def dummy_sample(list, n): return list[:n] class UnitTests(unittest.TestCase): def test_train_test_split_list_0_2(self): lst = [1, 2, 3] train, test = train_test_split_list(lst, 0.2, False, 0) assert_train = [1, 2] assert_test = [3] self.assertEqual(assert_train, train) self.assertEqual(assert_test, test) def test_train_test_split_list(self): lst = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] train, test = train_test_split_list(lst, 0.5, False, 0) assert_train = [1, 2, 3, 4, 5] assert_test = [6, 7, 8, 9, 10] self.assertEqual(assert_train, train) self.assertEqual(assert_test, test) def test_train_test_split_list_0(self): lst = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] train, test = train_test_split_list(lst, 0.0, False, 0) assert_train = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] assert_test = [] self.assertEqual(assert_train, train) self.assertEqual(assert_test, test) def test_train_test_split_list_1(self): lst = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] train, test = train_test_split_list(lst, 1.0, False, 0) assert_train = [] assert_test = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] self.assertEqual(assert_train, train) self.assertEqual(assert_test, test) def test_train_test_split_gt(self): gt = { 1: { 1: ["d", "e"], 2: ["f", "g"], }, 2: { 1: ["a", "d"], 2: ["b", "c"], } } train, test = train_test_split_gt(gt, 0.5, False, 0) assert_train = { 1: ["d", "f"], 2: ["a", "b"], } assert_test = { 1: ["e", "g"], 2: ["d", "c"], } self.assertEqual(assert_train, train) self.assertEqual(assert_test, test) def test_train_test_split_gt_0(self): gt = { 1: { 1: ["d", "e"], 2: ["f", "g"], }, 2: { 1: ["a", "d"], 2: ["b", "c"], } } train, test = train_test_split_gt(gt, 0.0, False, 0) assert_train = { 1: ["d", "e", "f", "g"], 2: ["a", "d", "b", "c"], } assert_test = { 1: [], 2: [] } self.assertEqual(assert_train, train) self.assertEqual(assert_test, test) def test_train_test_split_gt_1(self): gt = { 1: { 1: ["d", "e"], 2: ["f", "g"], }, 2: { 1: ["a", "d"], 2: ["b", "c"], } } train, test = train_test_split_gt(gt, 1.0, False, 0) assert_train = { 1: [], 2: [] } assert_test = { 1: ["d", "e", "f", "g"], 2: ["a", "d", "b", "c"], } self.assertEqual(assert_train, train) self.assertEqual(assert_test, test) if __name__ == '__main__': suite = unittest.TestLoader().loadTestsFromTestCase(UnitTests) unittest.TextTestRunner(verbosity=2).run(suite)
# -*- coding: utf-8 -*- """An exporter for signaling pathway impact analysis (SPIA) described by [Tarca2009]_. .. [Tarca2009] <NAME>., *et al* (2009). `A novel signaling pathway impact analysis <https://doi.org/10.1093/bioinformatics/btn577>`_. Bioinformatics, 25(1), 75–82. .. seealso:: https://bioconductor.org/packages/release/bioc/html/SPIA.html """ import itertools as itt import os from collections import OrderedDict from typing import Dict, Mapping, Set import pandas as pd from ..constants import ASSOCIATION, CAUSAL_DECREASE_RELATIONS, CAUSAL_INCREASE_RELATIONS, RELATION from ..dsl import CentralDogma, Gene, ListAbundance, ProteinModification, Rna from ..language import pmod_mappings from ..struct import BELGraph from ..typing import EdgeData __all__ = [ 'to_spia_dfs', 'to_spia_excel', 'to_spia_tsvs', ] SPIADataFrames = Mapping[str, pd.DataFrame] KEGG_RELATIONS = [ "activation", "compound", "binding/association", "expression", "inhibition", "activation_phosphorylation", "phosphorylation", "inhibition_phosphorylation", "inhibition_dephosphorylation", "dissociation", "dephosphorylation", "activation_dephosphorylation", "state change", "activation_indirect effect", "inhibition_ubiquination", "ubiquination", "expression_indirect effect", "inhibition_indirect effect", "repression", "dissociation_phosphorylation", "indirect effect_phosphorylation", "activation_binding/association", "indirect effect", "activation_compound", "activation_ubiquination" ] def to_spia_excel(graph: BELGraph, path: str) -> None: """Write the BEL graph as an SPIA-formatted excel sheet at the given path.""" x = to_spia_dfs(graph) spia_matrices_to_excel(x, path) def to_spia_tsvs(graph: BELGraph, directory: str) -> None: """Write the BEL graph as a set of SPIA-formatted TSV files in a given directory.""" x = to_spia_dfs(graph) spia_matrices_to_tsvs(x, directory) def to_spia_dfs(graph: BELGraph) -> SPIADataFrames: """Create an excel sheet ready to be used in SPIA software. :param graph: BELGraph :return: dictionary with matrices """ index_nodes = get_matrix_index(graph) spia_matrices = build_spia_matrices(index_nodes) for u, v, edge_data in graph.edges(data=True): # Both nodes are CentralDogma abundances if isinstance(u, CentralDogma) and isinstance(v, CentralDogma): # Update matrix dict update_spia_matrices(spia_matrices, u, v, edge_data) # Subject is CentralDogmaAbundance and node is ListAbundance elif isinstance(u, CentralDogma) and isinstance(v, ListAbundance): # Add a relationship from subject to each of the members in the object for node in v.members: # Skip if the member is not in CentralDogma if not isinstance(node, CentralDogma): continue update_spia_matrices(spia_matrices, u, node, edge_data) # Subject is ListAbundance and node is CentralDogmaAbundance elif isinstance(u, ListAbundance) and isinstance(v, CentralDogma): # Add a relationship from each of the members of the subject to the object for node in u.members: # Skip if the member is not in CentralDogma if not isinstance(node, CentralDogma): continue update_spia_matrices(spia_matrices, node, v, edge_data) # Both nodes are ListAbundance elif isinstance(u, ListAbundance) and isinstance(v, ListAbundance): for sub_member, obj_member in itt.product(u.members, v.members): # Update matrix if both are CentralDogma if isinstance(sub_member, CentralDogma) and isinstance(obj_member, CentralDogma): update_spia_matrices(spia_matrices, sub_member, obj_member, edge_data) # else Not valid edge return spia_matrices def get_matrix_index(graph: BELGraph) -> Set[str]: """Return set of HGNC names from Proteins/Rnas/Genes/miRNA, nodes that can be used by SPIA.""" # TODO: Using HGNC Symbols for now return { node.name for node in graph if isinstance(node, CentralDogma) and node.namespace.upper() == 'HGNC' } def build_spia_matrices(nodes: Set[str]) -> Dict[str, pd.DataFrame]: """Build an adjacency matrix for each KEGG relationship and return in a dictionary. :param nodes: A set of HGNC gene symbols :return: Dictionary of adjacency matrix for each relationship """ nodes = list(sorted(nodes)) # Create sheets of the excel in the given order matrices = OrderedDict() for relation in KEGG_RELATIONS: matrices[relation] = pd.DataFrame(0, index=nodes, columns=nodes) return matrices UB_NAMES = {"Ub"} | {e.name for e in pmod_mappings['Ub']['xrefs']} PH_NAMES = {"Ph"} | {e.name for e in pmod_mappings['Ph']['xrefs']} def update_spia_matrices( spia_matrices: Dict[str, pd.DataFrame], u: CentralDogma, v: CentralDogma, edge_data: EdgeData, ) -> None: """Populate the adjacency matrix.""" if u.namespace.lower() != 'hgnc' or v.namespace.lower() != 'hgnc': return u_name = u.name v_name = v.name relation = edge_data[RELATION] if relation in CAUSAL_INCREASE_RELATIONS: # If it has pmod check which one and add it to the corresponding matrix if v.variants and any(isinstance(variant, ProteinModification) for variant in v.variants): for variant in v.variants: if not isinstance(variant, ProteinModification): continue elif variant.entity.name in UB_NAMES: spia_matrices["activation_ubiquination"][u_name][v_name] = 1 elif variant.entity.name in PH_NAMES: spia_matrices["activation_phosphorylation"][u_name][v_name] = 1 elif isinstance(v, (Gene, Rna)): # Normal increase, add activation spia_matrices['expression'][u_name][v_name] = 1 else: spia_matrices['activation'][u_name][v_name] = 1 elif relation in CAUSAL_DECREASE_RELATIONS: # If it has pmod check which one and add it to the corresponding matrix if v.variants and any(isinstance(variant, ProteinModification) for variant in v.variants): for variant in v.variants: if not isinstance(variant, ProteinModification): continue elif variant.entity.name in UB_NAMES: spia_matrices['inhibition_ubiquination'][u_name][v_name] = 1 elif variant.entity.name in PH_NAMES: spia_matrices["inhibition_phosphorylation"][u_name][v_name] = 1 elif isinstance(v, (Gene, Rna)): # Normal decrease, check which matrix spia_matrices["repression"][u_name][v_name] = 1 else: spia_matrices["inhibition"][u_name][v_name] = 1 elif relation == ASSOCIATION: spia_matrices["binding_association"][u_name][v_name] = 1 def spia_matrices_to_excel(spia_matrices: SPIADataFrames, path: str) -> None: """Export a SPIA data dictionary into an Excel sheet at the given path. .. note:: # The R import should add the values: # ["nodes"] from the columns # ["title"] from the name of the file # ["NumberOfReactions"] set to "0" """ writer = pd.ExcelWriter(path, engine='xlsxwriter') for relation, df in spia_matrices.items(): df.to_excel(writer, sheet_name=relation, index=False) # Save excel writer.save() def spia_matrices_to_tsvs(spia_matrices: SPIADataFrames, directory: str) -> None: """Export a SPIA data dictionary into a directory as several TSV documents.""" os.makedirs(directory, exist_ok=True) for relation, df in spia_matrices.items(): df.to_csv(os.path.join(directory, '{relation}.tsv'.format(relation=relation)), index=True)
<filename>download.py import os, time, io, re, threading import requests from PIL import Image from PyPDF2 import PdfFileMerger header = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36' } def mkdir(path): path = path.strip() isExists = os.path.exists(path) if not isExists: os.makedirs(path) def search(name, page = 1): print('Searching ...') req = requests.post('http://www.sslibrary.com/book/search/do', headers = header, data = {'sw': name, 'allsw': '', 'searchtype': '', 'classifyId': '', 'isort': '', 'field': 1, 'jsonp': '', 'showcata': '', 'expertsw': '', 'bCon': '', 'page': page, 'pagesize': 10, 'sign': '', 'enc': ''}) data = req.json() result = [] pdfUrl = 'http://www.sslibrary.com/reader/pdf/pdfreader?ssid=%s&d=%s' imgUrl = 'http://www.sslibrary.com/reader/jpath/jpathreader?ssid=%s&d=%s' if data['success']: list = data['data']['result'] total = data['data']['total'] print('%d results in total\n' % total) for index, book in enumerate(list): print('[%d] %s | %s | %s' % (index, book['bookName'], book['publisher'], book['author'])) print('-------------------------------------------------------------') if book['isFromBW']: result.append({ 'name': book['bookName'], 'url': pdfUrl % (book['ssid'], book['jpathD']) }) else: result.append({ 'name': book['bookName'], 'url': imgUrl % (book['ssid'], book['jpathD']) }) if len(result) == 0: return False return result def getDownloadInfo(readerUrl): req = requests.get(readerUrl, headers = header) page = req.text page = page.replace('\r', '').replace('\n', '') isImg = 'jpath' in readerUrl if isImg: # jpg pices reg_url = re.compile('(?<=jpgPath: ")[^"]*') reg_total = re.compile('(?<=put">)\d+') total = reg_total.search(page).group() url = reg_url.search(page).group() url = 'http://img.sslibrary.com' + url + '%06d?zoom=0' return { 'url': url, 'total': int(total), 'isImg': True } # pdf pices reg_fileMark = re.compile('(?<=fileMark = ")\d+') reg_userMark = re.compile('(?<=userMark = ")\d*') reg_url = re.compile("(?<=DEFAULT_BASE_DOWNLOAD_URL = ')[^;]*") reg_total = re.compile('(?<=pages=)\d+') fileMark = reg_fileMark.search(page).group() userMark = reg_userMark.search(page).group() url = reg_url.search(page).group() url = url.replace("'", "").replace(' ', '') url = url.replace('+fileMark+', fileMark).replace('+userMark+', userMark) + '&cpage=%d' total = int(reg_total.search(url).group()) return { 'url': url, 'total': int(total), 'isImg': False } def downloadPDF(downloadInfo, toPath, threadNum = 8): top = 1 url = downloadInfo['url'] total = downloadInfo['total'] outName = toPath + '/page%d.pdf' mkdir(toPath) qLock = threading.Lock() def threadDownloadImg(): nonlocal top cur = 0 while True: # get a new page with qLock: if top > total: # all the pages are downloaded break cur = top top += 1 try: r = requests.get(url % cur, headers = header) im = Image.open(io.BytesIO(r.content)) im.save(outName % cur, 'PDF', dpi=im.info['dpi']) time.sleep(1) except: print('page %d failed!' % cur) def threadDownloadPDF(): nonlocal top cur = 0 while True: # get a new page with qLock: if top > total: # all the pages are downloaded break cur = top top += 1 try: r = requests.get(url % cur, headers = header) with open(outName % cur, 'wb+') as pice: pice.write(r.content) except: print('page %d failed!' % cur) print('Downloading ...') threadList = [] if downloadInfo['isImg']: for _ in range(threadNum): t = threading.Thread(target = threadDownloadImg) t.start() threadList.append(t) else: for _ in range(threadNum): t = threading.Thread(target = threadDownloadPDF) t.start() threadList.append(t) for i in range(threadNum): threadList[i].join() mergePDF(toPath, total, 'Merged') def mergePDF(path, num, name): merger = PdfFileMerger() for cpage in range(1, num + 1): merger.append(open(path + '/page%d.pdf' % cpage, 'rb')) merger.write(path + '/' + name + '.pdf') merger.close() if __name__ == '__main__': result = False while (result == False): keyword = input('Input a keyword without any spaces: ') result = search(keyword) if result == False: print('Nothing found!') else: break pages = 1 choice = input('Input the index of the file you want, press Enter to get the next page of the result of the search, or input -1 to get the last page: \n') while (choice == '' or choice == '-1'): if choice == '': pages = pages + 1 else: pages = pages - 1 if pages <= 0: print('The first page now!') pages = 1 result = search(keyword, pages) if result == False: print('Hit the last page!') pages = pages - 1 choice = input('Input the index of the file you want, press Enter to get the next page of the result of the search, or input -1 to get the last page: \n') choice = int(choice) downloadInfo = getDownloadInfo(result[choice]['url']) downloadPDF(downloadInfo, result[choice]['name'])
# -*- coding: future_fstrings -*- import unittest import hashlib import math import string import collections try: import unittest.mock as mock except ImportError: import mock from os import urandom from random import choice from merklelib.compat import is_py2 from merklelib import utils from merklelib.merkle import ( Hasher, MerkleNode, MerkleTree, AuditProof, AuditNode, LEFT, RIGHT, UNKNOWN, verify_leaf_inclusion, verify_tree_consistency ) def hashfunc(x): return hashlib.sha256(x).digest() # used for side effects def mirror(x): return x # Adding __dict__ would allow to mock prototype methods class MerkleNode(MerkleNode): __slots__ = ('__dict__', ) def patchdescriptor(cls, method, new): if not hasattr(cls, method): raise RuntimeError(f'{method} not found') def _patchdescriptor(func): def _wrapper(*args, **kwargs): try: current = getattr(cls, method) setattr(cls, method, new) result = func(*args, **kwargs) finally: setattr(cls, method, current) return _wrapper return _patchdescriptor def _calculate_root(nodes): hasher = Hasher(hashfunc) while len(nodes) > 1: if len(nodes) % 2 != 0: nodes.append(nodes[-1]) a = iter(nodes) nodes = [] for l, r in zip(a, a): hashval = l if hashval != r: hashval = hasher.hash_children(l, r) nodes.append(hashval) return nodes[0] # commonly used objects across tests hasher = Hasher(hashfunc) leaf = b'765f15d171871b00034ee55e48f' to_hex = mock.patch('merklelib.utils.to_hex', side_effect=mirror) to_hex.start() class MerkleTestCase(unittest.TestCase): def test_hasher(self): children = leaf * 2 classname = hasher.__class__.__name__ self.assertEqual(hasher.hash_leaf(leaf), hashfunc(b'\x00' + leaf)) self.assertEqual(hasher.hash_children(leaf, leaf), hashfunc(b'\x01'+children)) self.assertNotEqual(hasher.hashfunc, hashfunc) self.assertEqual(hasher.hashfunc.__wrapped__, hashfunc) self.assertEqual(str(hasher), f'{classname}({hashfunc})') self.assertEqual(repr(hasher), f'{classname}({hashfunc})') def test_merkle_node(self): hashval = hashfunc(leaf) left, right = MerkleNode(hashval), MerkleNode(hashval) node = MerkleNode(hashval, left=left, right=right) self.assertRaises(TypeError, MerkleNode, None) self.assertRaises(TypeError, MerkleNode, bytes()) self.assertEqual(left.parent, right.parent) self.assertEqual(left.hash, hashval) self.assertEqual(right.hash, hashval) self.assertEqual(left.type, LEFT) self.assertEqual(right.type, RIGHT) self.assertEqual(left.sibiling, right) self.assertEqual(right.sibiling, left) self.assertEqual(node.type, UNKNOWN) self.assertIsNone(node.sibiling) @patchdescriptor(MerkleNode, 'type', UNKNOWN) def test_merkle_node_combine(self): lefthash = hashfunc(b'\x01'+leaf) righthash = hashfunc(b'\x02'+leaf) left, right = MerkleNode(lefthash), MerkleNode(righthash) node = MerkleNode.combine(hasher, left, right) def _assert_all(finalhash): self.assertEqual(node.hash, finalhash) self.assertEqual(left.parent, right.parent) self.assertEqual(left.hash, lefthash) self.assertEqual(right.hash, righthash) self.assertEqual(left.sibiling, right) self.assertEqual(right.sibiling, left) _assert_all(hasher.hash_children(lefthash, righthash)) # testing concat(right, left) left.type = RIGHT; right.type = LEFT node = MerkleNode.combine(hasher, left, right) _assert_all(hasher.hash_children(righthash, lefthash)) # another case of concat(right, left) left.type = LEFT node = MerkleNode.combine(hasher, left, right) _assert_all(hasher.hash_children(righthash, lefthash)) def test_audit_proof(self): hashes = list(string.ascii_letters) nodes = [AuditNode(hash, choice([LEFT, RIGHT])) for hash in hashes] proof = AuditProof(nodes) items = ', '.join(hashes) items_str = f'{{{items}}}' self.assertEqual(proof.hex_nodes, hashes) self.assertEqual(repr(proof), items_str) self.assertEqual(str(proof), items_str) def test_merkle_tree_init(self): leaves = list(string.ascii_letters) hashes = [hasher.hash_leaf(leaf) for leaf in leaves] # testing _init_hashfunc self.assertRaises(TypeError, MerkleTree, leaves, leaves) tree = MerkleTree('a', mirror) self.assertEqual(tree.hasher.hashfunc.__wrapped__, mirror) tree = MerkleTree('a', None) self.assertIsNotNone(tree.hasher) # basic check tree = MerkleTree(leaves, hasher) self.assertEqual(tree.hexleaves, hashes) self.assertEqual(tree.merkle_root, _calculate_root(hashes)) # converting non iterable leaves to tuples tree = MerkleTree('a', hasher) self.assertEqual(tree.hexleaves, [hasher.hash_leaf('a')]) self.assertEqual(tree.merkle_root, hasher.hash_leaf('a')) # test case for empty root tree = MerkleTree() self.assertEqual(tree.hexleaves, []) self.assertEqual(tree.merkle_root, None) def test_merkle_tree_get_proof(self): chars = list(string.ascii_letters[:32]) tree = MerkleTree(chars, hasher) self.assertEqual(tree.get_proof('invalid'), AuditProof([])) # proof should contain log2 (n) of nodes for char in chars: expected = math.ceil(math.log(len(chars), 2)) # check for simple chars proof = tree.get_proof(char) self.assertEqual(len(proof), expected) # check for hashed leaves proof = tree.get_proof(hasher.hash_leaf(char)) self.assertEqual(len(proof), expected) def test_merkle_tree_update(self): chars = string.ascii_letters hash_mapping = collections.OrderedDict() for char in chars: hash_mapping[char] = hasher.hash_leaf(char) tree = MerkleTree(chars, hasher) initial_merkle_root = tree.merkle_root # calculate the merkle root manually from hashes current_root = _calculate_root(hash_mapping.values()) self.assertEqual(initial_merkle_root, current_root) self.assertRaises(KeyError, tree.update, 'invalid', 'a') for a, b in zip(chars, chars[::-1]): prev_merkle_root = current_root hash_mapping[a] = hasher.hash_leaf(b) # swap values tree.update(a, b) # calculate the merkle root manually from hashes current_root = _calculate_root(hash_mapping.values()) self.assertNotEqual(tree.merkle_root, initial_merkle_root) self.assertNotEqual(tree.merkle_root, prev_merkle_root) self.assertEqual(tree.merkle_root, current_root) # same thing for hash values for leaves tree.update(hasher.hash_leaf(a), hasher.hash_leaf(b)) self.assertNotEqual(tree.merkle_root, initial_merkle_root) self.assertNotEqual(tree.merkle_root, prev_merkle_root) self.assertEqual(tree.merkle_root, current_root) def test_merkle_tree_append(self): ascii = string.ascii_letters hashes = [hasher.hash_leaf(char) for char in ascii] tree = MerkleTree(hashobj=hasher) # hash as one string tree.append(ascii) expected_hash = hasher.hash_leaf(ascii) self.assertEqual(len(tree), 1) self.assertEqual(tree.merkle_root, expected_hash) # append every ascii character tree.clear(); tree.extend(ascii) expected_hash = _calculate_root(hashes) self.assertEqual(len(tree), len(ascii)) self.assertEqual(tree.merkle_root, expected_hash) # test all cases for limit in range(1, len(ascii)): tree.clear(); tree.extend(ascii[:limit]) expected_hash = _calculate_root(hashes[:limit]) self.assertEqual(len(tree), limit) self.assertEqual(tree.merkle_root, expected_hash) def test_merkle_tree_equals(self): a = MerkleTree(string.ascii_letters) b = MerkleTree(string.ascii_letters) self.assertEqual(a, b) self.assertTrue(a.__eq__(b)) self.assertTrue(b.__eq__(a)) a.append(1); b.append(2); self.assertIsNot(a, b) self.assertFalse(a.__eq__(b)) self.assertFalse(b.__eq__(a)) def test_verify_leaf_inclusion(self): self.assertRaises(TypeError, verify_leaf_inclusion) self.assertRaises(TypeError, verify_leaf_inclusion, None, None, hashfunc) tree = MerkleTree(string.ascii_letters, hasher) merkle_root = tree.merkle_root for leaf in string.ascii_letters: proof = tree.get_proof(leaf) hashval = hasher.hash_leaf(leaf) self.assertEqual(tree.get_proof(hashval), proof) self.assertTrue(verify_leaf_inclusion(leaf, proof, hashfunc, merkle_root)) def test_verify_tree_consistency(self): self.assertRaises(TypeError, verify_tree_consistency) self.assertFalse(verify_tree_consistency(MerkleTree(), None, 10)) tree = MerkleTree(string.ascii_letters) merkle_root = tree.merkle_root self.assertTrue(verify_tree_consistency(tree, merkle_root, len(tree))) for size in range(1, len(tree)): old_tree = MerkleTree(string.ascii_letters[:size]) merkle_root = old_tree.merkle_root self.assertTrue(verify_tree_consistency(tree, merkle_root, size))
<filename>habitat/tasks/rearrange/rearrange_grasp_manager.py #!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import magnum as mn import numpy as np from habitat.tasks.rearrange.utils import get_aabb from habitat_sim.physics import ( CollisionGroupHelper, CollisionGroups, RigidConstraintSettings, ) class RearrangeGraspManager: def __init__(self, sim, config): self._sim = sim self._snapped_obj_id = None self._snap_constraints = [] self._leave_info = None self._config = config def reset(self): self._leave_info = None # Setup the collision groups. UserGroup7 is the held object group, it # can interact with anything except for the robot. CollisionGroupHelper.set_mask_for_group( CollisionGroups.UserGroup7, ~CollisionGroups.Robot ) self.desnap() def is_violating_hold_constraint(self): if self._config.get("IGNORE_HOLD_VIOLATE", False): return False # Is the object firmly in the grasp of the robot? ee_pos = self._sim.robot.ee_transform.translation if self.is_grasped: obj_pos = self._sim.get_translation(self._snapped_obj_id) if np.linalg.norm(ee_pos - obj_pos) >= self._config.HOLD_THRESH: return True return False @property def is_grasped(self): return self._snapped_obj_id is not None def update(self): if self._leave_info is not None: ee_pos = self._sim.robot.ee_transform.translation dist = np.linalg.norm(ee_pos - self._leave_info[0]) if dist >= self._leave_info[1]: self.snap_rigid_obj.override_collision_group( CollisionGroups.Default ) def desnap(self, force=False): """Removes any hold constraints currently active.""" if len(self._snap_constraints) == 0: # No constraints to unsnap self._snapped_obj_id = None return if self._snapped_obj_id is not None: obj_bb = get_aabb(self.snap_idx, self._sim) if force: self.snap_rigid_obj.override_collision_group( CollisionGroups.Default ) else: self._leave_info = ( self._sim.get_translation(self._snapped_obj_id), max(obj_bb.size_x(), obj_bb.size_y(), obj_bb.size_z()), ) for constraint_id in self._snap_constraints: self._sim.remove_rigid_constraint(constraint_id) self._snap_constraints = [] self._snapped_obj_id = None @property def snap_idx(self): return self._snapped_obj_id @property def snap_rigid_obj(self): return self._sim.get_rigid_object_manager().get_object_by_id( self._snapped_obj_id ) def snap_to_obj(self, snap_obj_id: int, force: bool = True): """ :param snap_obj_id: Simulator object index. :force: Will transform the object to be in the robot's grasp, even if the object is already in the grasped state. """ if len(self._snap_constraints) != 0: # We were already grabbing something else. raise ValueError( f"Tried snapping to {snap_obj_id} when already snapped to {self._snapped_obj_id}" ) if force: # Set the transformation to be in the robot's hand already. self._sim.set_transformation( self._sim.robot.ee_transform, snap_obj_id ) if snap_obj_id == self._snapped_obj_id: # Already grasping this object. return self._snapped_obj_id = snap_obj_id # Set collision group to GraspedObject so that it doesn't collide # with the links of the robot. self.snap_rigid_obj.override_collision_group( CollisionGroups.UserGroup7 ) def create_hold_constraint(pivot_in_link, pivot_in_obj): c = RigidConstraintSettings() c.object_id_a = self._sim.robot.get_robot_sim_id() c.link_id_a = self._sim.robot.ee_link_id c.object_id_b = self._snapped_obj_id c.pivot_a = pivot_in_link c.pivot_b = pivot_in_obj c.max_impulse = self._config.GRASP_IMPULSE return self._sim.create_rigid_constraint(c) self._snap_constraints = [ create_hold_constraint(mn.Vector3(0.1, 0, 0), mn.Vector3(0, 0, 0)), create_hold_constraint( mn.Vector3(0.0, 0, 0), mn.Vector3(-0.1, 0, 0) ), create_hold_constraint( mn.Vector3(0.1, 0.0, 0.1), mn.Vector3(0.0, 0.0, 0.1) ), ] if any((x == -1 for x in self._snap_constraints)): raise ValueError("Created bad constraint")
"""ldap client""" from asyncio import AbstractEventLoop, get_event_loop from contextlib import asynccontextmanager from functools import partial from typing import Any, AsyncGenerator, Dict, List, Tuple, Union from ldap3 import ALL, ASYNC, SIMPLE, Connection, Server __all__ = ["LDAPConnection", "Result"] Controls = List[tuple] ChangeSet = Dict[str, List[Tuple[str, list]]] Result = Dict[str, Union[int, str, list]] class LDAPConnection: """ LDAP connection async wrapper Wrapper for ldap3 to provide asyncio bindings similar to bonsai Args: host: ldap server hostname port: ldap server port number user: user dn to bind to ldap with password_file: location of file containing password on disk client_strategy: client_strategy to connect to ldap with. ASYNC or MOCK_ASYNC excepted server: A LDAP server to be used, eg mock server """ def __init__( self, host: str, port: int, user: str, password_file: str, *, client_strategy: str = ASYNC, server: Server = None, loop: AbstractEventLoop = None, ): self.server = server or Server( host=host, port=port, use_ssl=False, get_info=ALL ) self.loop = loop or get_event_loop() with open(password_file, "r") as file: password = file.read().strip() self.connection = Connection( self.server, user=user, password=password, auto_bind="NONE", version=3, authentication=SIMPLE, client_strategy=client_strategy, auto_referrals=True, check_names=True, read_only=False, lazy=False, ) async def wait_for(self, msg_id: str) -> None: await self.loop.run_in_executor( None, partial(self.connection.get_respone, msg_id) ) @asynccontextmanager async def connect(self) -> AsyncGenerator["LDAPConnection", None]: """ connect to ldap """ try: self.connection.bind() yield self finally: self.connection.unbind() async def add(self, *args: Any, **kwargs: Any) -> Result: """ Async Add LDAP Entry ref: https://ldap3.readthedocs.io/en/latest/add.html """ await self.wait_for(self.connection.add(*args, **kwargs)) return self.connection.results async def delete(self, *args: Any, **kwargs: Any) -> Result: """ Async Delete LDAP Entry ref: https://ldap3.readthedocs.io/en/latest/delete.html """ await self.wait_for(self.connection.delete(*args, *kwargs)) return self.connection.results async def modify(self, *args: Any, **kwargs: Any) -> Result: """ Async Modify ldap entry ref: https://ldap3.readthedocs.io/en/latest/modify.html """ await self.wait_for(self.connection.modify(*args, *kwargs)) return self.connection.results async def modify_dn(self, *args: Any, **kwargs: Any) -> Result: """ Async Modify ldap entry ref: https://ldap3.readthedocs.io/en/latest/modifydn.html """ await self.wait_for(self.connection.modify(*args, *kwargs)) return self.connection.results async def search(self, *args: Any, **kwargs: Any) -> List[dict]: """ Async Search ldap entry ref: https://ldap3.readthedocs.io/en/latest/searches.html """ await self.wait_for(self.connection.search(*args, **kwargs)) return self.connection.response
import math import torch import torch.nn as nn from torch.nn import init from torch.autograd import Variable from torch.nn import Parameter from torch.nn import functional as F from torch.nn.modules.utils import _pair import slowfast.utils.logging as logging from .build import MODEL_REGISTRY from slowfast.models import rnns from slowfast.models import losses from .rnns import (hConvGRUCell, ConvLSTMCell, ConvLSTMCell_C, ConvLSTMCell_CG1x1, ConvLSTMCell_CGpool, ConvLSTMCell_CG1x1_noI, ConvLSTMCell_CG1x1_noF, ConvLSTMCell_CG1x1_noO ) # import slowfast.models.losses as losses # import slowfast.models.rnns as rnns # import kornia logger = logging.get_logger(__name__) __all__ = [ "SmallPredNet", ] def get_recurrent_cell(name): return getattr(rnns, name) @MODEL_REGISTRY.register() class SmallPredNet(nn.Module): def __init__(self, cfg): super(SmallPredNet, self).__init__() # all configs: # layers and filters # using E+/- or just E # loss and weights # eval # cfg.PREDNET.LAYERS # cfg.PREDNET.LOSSES losses = {} for i in range(len(cfg.PREDNET.LOSSES[0])): losses[cfg.PREDNET.LOSSES[0][i]] = cfg.PREDNET.LOSSES[1][i] self.losses = losses self.evals = cfg.PREDNET.EVALS rnn_cell_name = cfg.PREDNET.CELL R_channels = cfg.PREDNET.LAYERS # (3, 48, 96, 192) A_channels = cfg.PREDNET.LAYERS # (3, 48, 96, 192) A_channels[0] = 1 # print(self.losses, self.evals) # self.losses={ # 'FocalLoss': 1, # 'CPC': 1e-2, # 'smooth_l1_loss': 1, # } # self.evals=[ # 'mse', # 'Acc', # 'IoU' # ] self.r_channels = R_channels + [0] # # for convenience self.a_channels = A_channels self.n_layers = len(R_channels) rnn_cell = get_recurrent_cell(rnn_cell_name) # top_losses = ['', ] # bottom_losses for i in range(self.n_layers): cell = rnn_cell(2 * self.a_channels[i] + self.r_channels[i+1], self.r_channels[i], (3, 3)) setattr(self, 'cell{}'.format(i), cell) for i in range(self.n_layers): if i == 0: if 'FocalLoss' in self.losses or 'CrossEntropy' in self.losses: fan_out = 2 # temporary conv = nn.Sequential( nn.Conv2d(self.r_channels[i], self.r_channels[i], 3, padding=1), nn.ReLU(), nn.Conv2d(self.r_channels[i], fan_out, 1), ) init.orthogonal_(conv[0].weight) init.constant_(conv[0].bias, 0) init.xavier_normal_(conv[2].weight) init.constant_(conv[2].bias, 0) # nn.init.xavier_uniform_(conv[2].weight) # nn.init.normal_(conv[2].weight, std=0.001) # nn.init.constant_(conv[2].bias, 0) # init.constant_(conv[2].bias[0], -0.1) # init.constant_(conv[2].bias[1], 0.1) # # original # conv = nn.Conv2d(self.r_channels[i], fan_out, 3, padding=1) #2 for focal loss # init.xavier_normal_(conv.weight) # init.constant_(conv.bias, 0) # init.constant_(conv.bias, torch.log(torch.tensor((1 - 0.01) / 0.01))) # init.constant_(conv.bias[0], torch.log(torch.tensor((1 - 0.01) / 0.01))) # init.constant_(conv.bias[1], 0) elif 'BinaryCrossEntropy' in self.losses: fan_out = 1 conv = nn.Conv2d(self.r_channels[i], fan_out, 3, padding=1) #2 for focal loss init.xavier_normal_(conv.weight) # init.constant_(conv.bias, torch.log(torch.tensor((1 - 0.01) / 0.01))) init.constant_(conv.bias, 0) else: conv = nn.Sequential(nn.Conv2d(self.r_channels[i], self.a_channels[i], 3, padding=1), nn.ReLU()) #2 for focal loss conv.add_module('satlu', SatLU()) init.orthogonal_(conv[0].weight) init.constant_(conv[0].bias, 0) else: conv = nn.Sequential(nn.Conv2d(self.r_channels[i], self.a_channels[i], 3, padding=1), nn.ReLU()) init.orthogonal_(conv[0].weight) init.constant_(conv[0].bias, 0) setattr(self, 'conv{}'.format(i), conv) self.upsample = nn.Upsample(scale_factor=2) self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2) for l in range(self.n_layers - 1): update_A = nn.Sequential(nn.Conv2d(2* self.a_channels[l], self.a_channels[l+1], (3, 3), padding=1), self.maxpool) init.orthogonal_(update_A[0].weight) init.constant_(update_A[0].bias, 0) setattr(self, 'update_A{}'.format(l), update_A) self.reset_parameters() self.cpc_gn = cfg.PREDICTIVE.CPC if self.cpc_gn: self.cpc_steps = cfg.PREDICTIVE.CPC_STEPS fan_in = R_channels[-1] wf = nn.Conv2d(fan_in, fan_in//4, kernel_size=1) init.orthogonal_(wf.weight) init.constant_(wf.bias, 0) self.add_module('cpc_target_layer', wf) self.cpc_pred_layer = {} for step in self.cpc_steps: wf = nn.Conv2d(fan_in, fan_in//4, kernel_size=1) init.orthogonal_(wf.weight) init.constant_(wf.bias, 0) self.add_module('cpc_pred_layer_%d'%step, wf) self.cpc_pred_layer[step] = wf # self.spatial_transforms = {} # self.feature_transforms = {} # for step in self.cpc_steps: # ws = SpatialTransformer(fan_in=fan_in) # wf = nn.Conv2d(fan_in, fan_in, kernel_size=1) # self.add_module('bu_warp_%d'%step, ws) # self.add_module('bu_feat_%d'%step, wf) # self.spatial_transforms[step] = ws # self.feature_transforms[step] = wf # motion readout -> depends on specs in cfg # predicts velocity # main losses # ff loss -> cpc moco # fb loss -> CE or focal loss # auxilary losses # classification loss -> need to group all classes seen during one training # momentary motion -> speed # long term motion -> acceleration # integration (accumulated distance) # beginning to end (difference) # eval # Encoding # momentary motion at least # pixel error (Acc) # IoU # mse difference # Extrapolation # pixel error / timestep (Acc) # IoU / timestep # mse difference / timestep # motion_labels= [ # 'translate_v': 2, # [-1, 1] # 'translate_a': 2, # [-1, 1] # 'translate_distance': 1, # [0, +] # 'translate_difference': 2, # [-1, 1] # 'rotate_v': 1, # [-1, 1] # 'rotate_a': 1, # [-1, 1] # 'rotate_distance': 1, # [-, +] # 'rotate_difference': 1, # [-, +] # 'expand_v': 1, # [-1, 1] # 'expand_a': 1, # [-1, 1] # 'expand_distance': 1, # [-1, 1] # 'expand_difference': 1, # [-1, 1] # ] # fan_motion_labels = # self.prediction_head = nn.Sequential( # nn.AdaptiveMaxPool2d((1,1)), # nn.Linear(R_channels[-1], motion_labels) # ) def reset_parameters(self): for l in range(self.n_layers): cell = getattr(self, 'cell{}'.format(l)) cell.reset_parameters() def forward(self, input, meta=None, extra=[], autoreg=False): ########################### # Initialize ########################### if isinstance(input, list): input = input[0] R_seq = [None] * self.n_layers H_seq = [None] * self.n_layers E_seq = [None] * self.n_layers w, h = input.size(-2), input.size(-1) batch_size = input.size(0) for l in range(self.n_layers): # E_seq[l] = torch.zeros(batch_size, 2*self.a_channels[l], w, h).to(input.device) E_seq[l] = input.new_zeros([batch_size, 2*self.a_channels[l], w, h]) # R_seq[l] = torch.zeros(batch_size, self.r_channels[l], w, h).to(input.device) R_seq[l] = input.new_zeros([batch_size, self.r_channels[l], w, h]) w = w//2 h = h//2 time_steps = input.size(2) total_error = [] frames = [] frame_error = 0 outputs = {} ########################### # Loop ########################### for t in range(time_steps): A = input[:,:,t] #t A = A.float() ########################### # Top Down ########################### for l in reversed(range(self.n_layers)): cell = getattr(self, 'cell{}'.format(l)) if t == 0: E = E_seq[l] R = R_seq[l] hx = (R, R) else: E = E_seq[l] R = R_seq[l] hx = H_seq[l] if l == self.n_layers - 1: R, hx = cell(E, hx) else: tmp = torch.cat((E, self.upsample(R_seq[l+1])), 1) R, hx = cell(tmp, hx) R_seq[l] = R H_seq[l] = hx if l == self.n_layers-1: R_top = R ########################### # Bottom up ########################### for l in range(self.n_layers): conv = getattr(self, 'conv{}'.format(l)) A_hat = conv(R_seq[l]) # if l == 0: # frame_prediction = A_hat # if t>0: # frame_error += torch.pow(A_hat - A, 2).detach().mean((1,2,3)) if l == 0: A_h_0 = A_hat A_0 = A A_hat = torch.argmax(A_hat, dim=1)[:,None] if A_hat.shape[1] != 1 else A_hat # A_hat = F.softmax(A_hat, dim=1)[:,1][:,None] if A_hat.shape[1] != 1 else A_hat pos = F.relu(A_hat - A) neg = F.relu(A - A_hat) E = torch.cat([pos, neg],1) E_seq[l] = E if l < self.n_layers - 1: update_A = getattr(self, 'update_A{}'.format(l)) A = update_A(E) ########################### # Gather loop variables ########################### outputs = self.get_loss_t(outputs, t, R_top, E_seq, A_0, A_h_0, extra, time_steps) ########################### # Calculate loss and process output ########################### outputs = self.get_loss(input, outputs, extra) return outputs # def readout_motion(self, R): # """ readout motion variables: velocity , traveled distance # """ # return self.prediction_head(R) def get_loss_t(self, outputs, i, R, E_seq, frame, A_hat, extra, timesteps): if i == 0: ############################ # FeedForward loss variables ############################ if 'CPC' in self.losses: cpc_targets = [] cpc_preds = {step: [] for step in self.cpc_steps} outputs['cpc_targets'] = cpc_targets outputs['cpc_preds'] = cpc_preds ############################ # Feedback loss variables ############################ if 'CrossEntropy' in self.losses: outputs['ce_loss'] = [] if 'FocalLoss' in self.losses: outputs['focal_loss'] = [] if 'BinaryCrossEntropy' in self.losses: outputs['bce_loss'] = [] if 'L1Loss' in self.losses: outputs['errors'] = [] ############################ # Evaluation ############################ outputs['mse'] = [] # outputs['Acc'] = [] outputs['IoU'] = [] outputs['prec'] = [] outputs['recall'] = [] outputs['f1s'] = [] outputs['balacc'] = [] outputs['frames'] = [] else: ############################ # Feedback loss variables ############################ if 'CrossEntropy' in self.losses: pos = ((frame>=0.2)*1).sum() neg = ((frame<0.2)*1).sum() pos = pos/(pos+neg) neg = neg/(pos+neg) fb_loss = F.cross_entropy(A_hat.permute([0,2,3,1]).reshape([-1,2]), ((frame<0.2)*1).long().flatten(), weight=A_hat.new([pos, neg])) outputs['ce_loss'].append(fb_loss) if 'FocalLoss' in self.losses: # print('target', frame.shape) # print('target', frame.min(), frame.max()) # print('input', A_hat.shape) # pos = ((frame>=0.2)*1).sum() # neg = ((frame<0.2)*1).sum() # fb_loss = F.cross_entropy(A_hat, ((frame>=0.2)*1).long() weight=torch.Tensor([pos, neg])) fb_loss = losses.focal_loss(A_hat, (frame>=0.2)*1, gamma=2.0, reduce=True) # .view([frame.shape[0], -1]).mean(-1) # A_hat = torch.argmax(A_hat, dim=1)[:,None] # A_hat = F.softmax(A_hat, dim=1)[:,1][:,None] outputs['focal_loss'].append(fb_loss) if 'BinaryCrossEntropy' in self.losses: pos = ((frame>=0.2)*1) neg = ((frame<0.2)*1) n_pos = pos.sum() n_neg = neg.sum() n_pos = n_pos/(n_pos+n_neg) n_neg = n_neg/(n_pos+n_neg) mask = pos*n_neg + n_pos*neg fb_loss = F.binary_cross_entropy_with_logits(A_hat, frame, reduction='none') * mask fb_loss = fb_loss.mean() outputs['bce_loss'].append(fb_loss) if 'L1Loss' in self.losses: fb_loss = torch.cat([torch.mean(e.view(e.size(0), -1), 1, keepdim=True) for e in E_seq], 1) outputs['errors'].append(fb_loss) ############################ # FeedForward loss variables ############################ if 'CPC' in self.losses: # CPC between R:t and R:t+4 if i >= min(self.cpc_steps)+1: outputs['cpc_targets'].append(self.cpc_target_layer(R)) # print('targ', i) for step in self.cpc_steps: if i < timesteps-step: # print('preds', i, step) outputs['cpc_preds'][step].append(self.cpc_pred_layer[step](R)) ############################ # Auxilary losses ############################ # if 'smooth_l1_loss' in self.losses: # # get_labels() # ff_loss = F.smooth_l1_loss(A, frame) # outputs['ff_loss'] = ff_loss # outputs['mean_error'].append(mean_error) # print(A_hat[:,:10]) # print(frame[:,:10]) A_hat = A_hat.data #detach() frame = frame.data #detach() # print(A_hat.shape) ############################ # Evaluation ############################ if 'FocalLoss' in self.losses or 'CrossEntropy' in self.losses: outputs['mse'].append(F.mse_loss(F.softmax(A_hat, dim=1)[:,1][:,None], frame, reduction='none').view([A_hat.shape[0], -1]).mean(-1)) # outputs['Acc'].append(losses.pixel_accuracy(A_hat, frame)) balacc, precision, recall, f1s = losses.acc_scores((frame>0.2).long(), A_hat) A_hat = torch.argmax(A_hat, dim=1)[:,None].float() outputs['IoU'].append(losses.intersectionAndUnion(A_hat, frame)) elif 'BinaryCrossEntropy' in self.losses: A_hat = torch.sigmoid(A_hat) outputs['mse'].append(F.mse_loss(A_hat, frame, reduction='none').view([A_hat.shape[0], -1]).mean(-1)) # outputs['Acc'].append(losses.pixel_accuracy(A_hat, frame)) outputs['IoU'].append(losses.intersectionAndUnion(A_hat, frame)) balacc, precision, recall, f1s = losses.metric_scores((frame>0.2).long().byte(), (A_hat>0.2).long().byte()) balacc = balacc * 100 else: outputs['mse'].append(F.mse_loss(A_hat, frame, reduction='none').view([A_hat.shape[0], -1]).mean(-1)) # outputs['Acc'].append(losses.pixel_accuracy(A_hat, frame)) outputs['IoU'].append(losses.intersectionAndUnion(A_hat, frame)) balacc, precision, recall, f1s = losses.metric_scores((frame>0.2).long().byte(), (A_hat>0.2).long().byte()) balacc = balacc * 100 outputs['prec'].append(precision) outputs['recall'].append(recall) outputs['f1s'].append(f1s) outputs['balacc'].append(balacc) if 'frames' in extra: outputs['frames'].append(A_hat) return outputs def get_loss(self, input, outputs, extra): output = {} total_loss = 0 ############################ # Feedback Loss ############################ if 'CrossEntropy' in self.losses: output['ce_loss'] = torch.stack(outputs['ce_loss']).mean() total_loss += output['ce_loss'] * self.losses['CrossEntropy'] if 'FocalLoss' in self.losses: output['focal_loss'] = torch.stack(outputs['focal_loss']).mean() total_loss += output['focal_loss'] * self.losses['FocalLoss'] if 'BinaryCrossEntropy' in self.losses: output['bce_loss'] = torch.stack(outputs['bce_loss']).mean() total_loss += output['bce_loss'] * self.losses['BinaryCrossEntropy'] if 'L1Loss' in self.losses: output['errors'] = torch.stack(outputs['errors'], 2)*torch.Tensor([1]+[0.1]*(self.n_layers-1)).to(outputs['errors'][0].device)[None,:,None] output['errors'] = output['errors'].sum(1).mean() total_loss += output['errors'] * self.losses['L1Loss'] ############################ # Feedforward Loss ############################ if 'CPC' in self.losses: cpc_targets = outputs['cpc_targets'] cpc_preds = outputs['cpc_preds'] cpc_loss = losses.cpc_loss(cpc_preds, cpc_targets) output['cpc_loss'] = cpc_loss total_loss += output['cpc_loss'] * self.losses['CPC'] # for step in self.cpc_steps: # if len(cpc_preds[step])>1: # cpc_preds[step] = torch.stack(cpc_preds[step], -1).transpose(1,4) # # .permute(1,0,3,4,2) #T B C H W -> B T H W C # # logger.info(cpc_preds[step].shape) # cpc_preds[step] = cpc_preds[step].reshape([-1,cpc_preds[step].shape[-1]]) # -> N C # # logger.info(cpc_targets[:,step-min(self.cpc_steps):].shape) # cpc_output = torch.matmul(cpc_targets[:,step-min(self.cpc_steps):].reshape([-1, cpc_preds[step].shape[-1]]), cpc_preds[step].t()) # labels = torch.cumsum(torch.ones_like(cpc_preds[step][:,0]).long(), 0) -1 # cpc_loss = cpc_loss + F.cross_entropy(cpc_output, labels) ############################ # Auxilary Losses ############################ # chosen losses from cfg ? how to integrate this information ? # motion labels from meta: translation, rotation, speed, acceleration # if not self.supervised: # R = R.detach() # motion_outputs = self.readout_motion(R) output['total_loss'] = total_loss ############################ # Evaluation ############################ output['mse'] = torch.stack(outputs['mse'], 1).mean() - F.mse_loss(input[:,:,2:], input[:,:,1:-1]) # output['Acc'] = torch.stack(outputs['Acc'], 1).float().mean() # output['IoU'] = torch.stack(outputs['IoU'], 1).float().mean() # output['prec'] = torch.stack(outputs['prec'], 0).float().mean() output['recall'] = torch.stack(outputs['recall'], 0).float().mean() output['f1s'] = torch.stack(outputs['f1s'], 0).float().mean() output['balacc'] = torch.stack(outputs['balacc'], 0).float().mean() if 'frames' in extra: output['frames'] = torch.stack(outputs['frames'], 2) return output class SatLU(nn.Module): def __init__(self, lower=0, upper=255, inplace=False): super(SatLU, self).__init__() self.lower = lower self.upper = upper self.inplace = inplace def forward(self, input): return F.hardtanh(input, self.lower, self.upper, self.inplace) def __repr__(self): inplace_str = ', inplace' if self.inplace else '' return self.__class__.__name__ + ' ('\ + 'min_val=' + str(self.lower) \ + ', max_val=' + str(self.upper) \ + inplace_str + ')' # https://gist.github.com/Kaixhin/57901e91e5c5a8bac3eb0cbbdd3aba81 def info(prefix, var): logger.info('-------{}----------'.format(prefix)) logger.info('size: ', var.shape) logger.info('data type: ', type(var.data)) logger.info(type(var))
# -*- coding: utf-8 -*- # Description: bind rndc netdata python.d module # Author: l2isbad from base import SimpleService from re import compile, findall from os.path import getsize, isfile, split from os import access as is_accessible, R_OK from subprocess import Popen priority = 60000 retries = 60 update_every = 30 DIRECTORIES = ['/bin/', '/usr/bin/', '/sbin/', '/usr/sbin/'] NMS = ['requests', 'responses', 'success', 'auth_answer', 'nonauth_answer', 'nxrrset', 'failure', 'nxdomain', 'recursion', 'duplicate', 'rejections'] QUERIES = ['RESERVED0', 'A', 'NS', 'CNAME', 'SOA', 'PTR', 'MX', 'TXT', 'X25', 'AAAA', 'SRV', 'NAPTR', 'A6', 'DS', 'RRSIG', 'DNSKEY', 'SPF', 'ANY', 'DLV'] class Service(SimpleService): def __init__(self, configuration=None, name=None): SimpleService.__init__(self, configuration=configuration, name=name) self.named_stats_path = self.configuration.get('named_stats_path', '/var/log/bind/named.stats') self.regex_values = compile(r'([0-9]+) ([^\n]+)') # self.options = ['Incoming Requests', 'Incoming Queries', 'Outgoing Queries', # 'Name Server Statistics', 'Zone Maintenance Statistics', 'Resolver Statistics', # 'Cache DB RRsets', 'Socket I/O Statistics'] self.options = ['Name Server Statistics', 'Incoming Queries', 'Outgoing Queries'] self.regex_options = [r'(%s(?= \+\+)) \+\+([^\+]+)' % option for option in self.options] try: self.rndc = [''.join([directory, 'rndc']) for directory in DIRECTORIES if isfile(''.join([directory, 'rndc']))][0] except IndexError: self.rndc = False def check(self): # We cant start without 'rndc' command if not self.rndc: self.error('Command "rndc" not found') return False # We cant start if stats file is not exist or not readable by netdata user if not is_accessible(self.named_stats_path, R_OK): self.error('Cannot access file %s' % self.named_stats_path) return False size_before = getsize(self.named_stats_path) run_rndc = Popen([self.rndc, 'stats'], shell=False) run_rndc.wait() size_after = getsize(self.named_stats_path) # We cant start if netdata user has no permissions to run 'rndc stats' if not run_rndc.returncode: # 'rndc' was found, stats file is exist and readable and we can run 'rndc stats'. Lets go! self.create_charts() # BIND APPEND dump on every run 'rndc stats' # that is why stats file size can be VERY large if update_interval too small dump_size_24hr = round(86400 / self.update_every * (int(size_after) - int(size_before)) / 1048576, 3) # If update_every too small we should WARN user if self.update_every < 30: self.info('Update_every %s is NOT recommended for use. Increase the value to > 30' % self.update_every) self.info('With current update_interval it will be + %s MB every 24hr. ' 'Don\'t forget to create logrotate conf file for %s' % (dump_size_24hr, self.named_stats_path)) self.info('Plugin was started successfully.') return True else: self.error('Not enough permissions to run "%s stats"' % self.rndc) return False def _get_raw_data(self): """ Run 'rndc stats' and read last dump from named.stats :return: tuple( file.read() obj, named.stats file size ) """ try: current_size = getsize(self.named_stats_path) except OSError: return None, None run_rndc = Popen([self.rndc, 'stats'], shell=False) run_rndc.wait() if run_rndc.returncode: return None, None try: with open(self.named_stats_path) as bind_rndc: bind_rndc.seek(current_size) result = bind_rndc.read() except OSError: return None, None else: return result, current_size def _get_data(self): """ Parse data from _get_raw_data() :return: dict """ raw_data, size = self._get_raw_data() if raw_data is None: return None rndc_stats = dict() # Result: dict. # topic = Cache DB RRsets; body = A 178303 NS 86790 ... ; desc = A; value = 178303 # {'Cache DB RRsets': [('A', 178303), ('NS', 286790), ...], # {Incoming Queries': [('RESERVED0', 8), ('A', 4557317680), ...], # ...... for regex in self.regex_options: rndc_stats.update({topic: [(desc, int(value)) for value, desc in self.regex_values.findall(body)] for topic, body in findall(regex, raw_data)}) nms = dict(rndc_stats.get('Name Server Statistics', [])) inc_queries = {'i' + k: 0 for k in QUERIES} inc_queries.update({'i' + k: v for k, v in rndc_stats.get('Incoming Queries', [])}) out_queries = {'o' + k: 0 for k in QUERIES} out_queries.update({'o' + k: v for k, v in rndc_stats.get('Outgoing Queries', [])}) to_netdata = dict() to_netdata['requests'] = sum([v for k, v in nms.items() if 'request' in k and 'received' in k]) to_netdata['responses'] = sum([v for k, v in nms.items() if 'responses' in k and 'sent' in k]) to_netdata['success'] = nms.get('queries resulted in successful answer', 0) to_netdata['auth_answer'] = nms.get('queries resulted in authoritative answer', 0) to_netdata['nonauth_answer'] = nms.get('queries resulted in non authoritative answer', 0) to_netdata['nxrrset'] = nms.get('queries resulted in nxrrset', 0) to_netdata['failure'] = sum([nms.get('queries resulted in SERVFAIL', 0), nms.get('other query failures', 0)]) to_netdata['nxdomain'] = nms.get('queries resulted in NXDOMAIN', 0) to_netdata['recursion'] = nms.get('queries caused recursion', 0) to_netdata['duplicate'] = nms.get('duplicate queries received', 0) to_netdata['rejections'] = nms.get('recursive queries rejected', 0) to_netdata['stats_size'] = size to_netdata.update(inc_queries) to_netdata.update(out_queries) return to_netdata def create_charts(self): self.order = ['stats_size', 'bind_stats', 'incoming_q', 'outgoing_q'] self.definitions = { 'bind_stats': { 'options': [None, 'Name Server Statistics', 'stats', 'Name Server Statistics', 'bind_rndc.stats', 'line'], 'lines': [ ]}, 'incoming_q': { 'options': [None, 'Incoming queries', 'queries','Incoming queries', 'bind_rndc.incq', 'line'], 'lines': [ ]}, 'outgoing_q': { 'options': [None, 'Outgoing queries', 'queries','Outgoing queries', 'bind_rndc.outq', 'line'], 'lines': [ ]}, 'stats_size': { 'options': [None, '%s file size' % split(self.named_stats_path)[1].capitalize(), 'megabytes', '%s size' % split(self.named_stats_path)[1].capitalize(), 'bind_rndc.size', 'line'], 'lines': [ ["stats_size", None, "absolute", 1, 1048576] ]} } for elem in QUERIES: self.definitions['incoming_q']['lines'].append(['i' + elem, elem, 'incremental']) self.definitions['outgoing_q']['lines'].append(['o' + elem, elem, 'incremental']) for elem in NMS: self.definitions['bind_stats']['lines'].append([elem, None, 'incremental'])
## TrueTypeFont ## import struct from pdfmajor.execptions import CMapNotFound from pdfmajor.parser.cmapdb import FileUnicodeMap class TrueTypeFont(object): def __init__(self, name, fp): self.name = name self.fp = fp self.tables = {} self.fonttype = fp.read(4) try: (ntables, _1, _2, _3) = struct.unpack('>HHHH', fp.read(8)) for _ in range(ntables): (name, _, offset, length) = struct.unpack('>4sLLL', fp.read(16)) self.tables[name] = (offset, length) except struct.error: # Do not fail if there are not enough bytes to read. Even for # corrupted PDFs we would like to get as much information as # possible, so continue. pass return def create_unicode_map(self): if 'cmap' not in self.tables: raise CMapNotFound (base_offset, _) = self.tables['cmap'] fp = self.fp fp.seek(base_offset) (_, nsubtables) = struct.unpack('>HH', fp.read(4)) subtables = [] for i in range(nsubtables): subtables.append(struct.unpack('>HHL', fp.read(8))) char2gid = {} # Only supports subtable type 0, 2 and 4. for (_1, _2, st_offset) in subtables: fp.seek(base_offset+st_offset) (fmttype, _, _) = struct.unpack('>HHH', fp.read(6)) if fmttype == 0: char2gid.update(enumerate(struct.unpack('>256B', fp.read(256)))) elif fmttype == 2: subheaderkeys = struct.unpack('>256H', fp.read(512)) firstbytes = [0]*8192 for (i, k) in enumerate(subheaderkeys): firstbytes[k//8] = i nhdrs = max(subheaderkeys)//8 + 1 hdrs = [] for i in range(nhdrs): (firstcode, entcount, delta, offset) = struct.unpack('>HHhH', fp.read(8)) hdrs.append((i, firstcode, entcount, delta, fp.tell()-2+offset)) for (i, firstcode, entcount, delta, pos) in hdrs: if not entcount: continue first = firstcode + (firstbytes[i] << 8) fp.seek(pos) for c in range(entcount): gid = struct.unpack('>H', fp.read(2)) if gid: gid += delta char2gid[first+c] = gid elif fmttype == 4: (segcount, _1, _2, _3) = struct.unpack('>HHHH', fp.read(8)) segcount //= 2 ecs = struct.unpack('>%dH' % segcount, fp.read(2*segcount)) fp.read(2) scs = struct.unpack('>%dH' % segcount, fp.read(2*segcount)) idds = struct.unpack('>%dh' % segcount, fp.read(2*segcount)) pos = fp.tell() idrs = struct.unpack('>%dH' % segcount, fp.read(2*segcount)) for (ec, sc, idd, idr) in zip(ecs, scs, idds, idrs): if idr: fp.seek(pos+idr) for c in range(sc, ec+1): char2gid[c] = (struct.unpack('>H', fp.read(2))[0] + idd) & 0xffff else: for c in range(sc, ec+1): char2gid[c] = (c + idd) & 0xffff else: assert False, str(('Unhandled', fmttype)) # create unicode map unicode_map = FileUnicodeMap() for (char, gid) in char2gid.items(): unicode_map.add_cid2unichr(gid, char) return unicode_map
<filename>generator.py<gh_stars>0 from PIL import Image, ImageDraw import math import noise import random import numpy as np MAP_SIZE = (512, 512) SCALE = 256 EXPO_HEIGHT = 2 COLORS = { "grass" : (34,139,34), "forest" : (0, 100, 0), "sand" : (238, 214, 175), "water" : (65,105,225), "rock" : (139, 137, 137), "snow" : (255, 250, 250) } lut_vectors = ( (-1, 1), (0, 1), (1, 1), (-1, 0), (1, 0), (-1, -1), (0, -1), (1, -1) ) def update_point(coords, seed): return noise.snoise2(coords[0]/SCALE, coords[1]/SCALE, octaves=6, persistence=0.5, lacunarity=2, repeatx=MAP_SIZE[0], repeaty=MAP_SIZE[1], base=330 ) def normalize(input_map, minimum, maximum, expo): scale = maximum - minimum output_map = np.zeros(MAP_SIZE) for x in range(MAP_SIZE[0]): for y in range(MAP_SIZE[1]): output_map[x][y] = ((input_map[x][y] - minimum)/scale)**expo return output_map def generate_heightmap(): seed = int(random.random()*1000) minimum = 0 maximum = 0 heightmap = np.zeros(MAP_SIZE) for x in range(MAP_SIZE[0]): for y in range(MAP_SIZE[1]): new_value = update_point((x, y), seed) heightmap[x][y] = new_value if new_value < minimum: minimum = new_value if new_value > maximum: maximum = new_value print("Height map generated with seed:", seed) return normalize(heightmap, minimum, maximum, EXPO_HEIGHT) def out_of_bounds(coord): if coord[0] < 0 or coord[0] >= MAP_SIZE[0]: return True if coord[1] < 0 or coord[1] >= MAP_SIZE[1]: return True return False def generate_slopemap(heightmap): slopemap = np.zeros(MAP_SIZE) minimum = 0 maximum = 0 for x in range(MAP_SIZE[0]): for y in range(MAP_SIZE[1]): slope = 0 for vector in lut_vectors: coord = (x+vector[0], y+vector[1]) if out_of_bounds(coord): continue slope += abs(heightmap[x][y]-heightmap[coord[0]][coord[1]]) slope = slope/8 slopemap[x][y] = slope if slope < minimum: minimum = slope if slope > maximum: maximum = slope print("Slopemap generated") return normalize(slopemap, minimum, maximum, 1) def get_color(height, slope): if height > 0.2 and height < 0.9 and slope > 0.45: return COLORS["rock"] if height <= 0.2: return COLORS["water"] elif height > 0.2 and height <= 0.225: return COLORS["sand"] elif height > 0.225 and height <= 0.45: return COLORS["grass"] elif height > 0.45 and height <= 0.85: return COLORS["forest"] elif height > 0.85 and height <= 0.9: return COLORS["rock"] elif height > 0.9: return COLORS["snow"] def generate_vertices(heightmap): vertices = [] base = (-1, -0.75, -1) size = 2 max_height = 0.5 step_x = size/(MAP_SIZE[0]-1) step_y = size/(MAP_SIZE[1]-1) for x in range(MAP_SIZE[0]): for y in range(MAP_SIZE[1]): x_coord = base[0] + step_x*x y_coord = base[1] + max_height*heightmap[x][y] z_coord = base[2] + step_y*y vertices.append((x_coord, y_coord, z_coord)) print("Vertices generated") return vertices def generate_tris(): edges = [] surfaces = [] for x in range(MAP_SIZE[0]-1): for y in range(MAP_SIZE[1]-1): base = x*MAP_SIZE[0]+y a = base b = base+1 c = base+MAP_SIZE[0]+1 d = base+MAP_SIZE[0] edges.append((a, b)) edges.append((b, c)) edges.append((c, a)) edges.append((c, d)) edges.append((d, a)) surfaces.append((a, b, c)) surfaces.append((a, c, d)) print("Edges, surfaces generated") return edges, surfaces def export_norm_map(norm_map, filename): image = Image.new('RGB', MAP_SIZE, 0) draw = ImageDraw.ImageDraw(image) for x in range(MAP_SIZE[0]): for y in range(MAP_SIZE[1]): color = int(norm_map[x][y]*255) draw.point((x, y), (color, color, color)) image.save(filename) print(filename, "saved") return def export_texture(heightmap, slopemap, filename): image = Image.new('RGB', MAP_SIZE, 0) draw = ImageDraw.ImageDraw(image) for x in range(MAP_SIZE[0]): for y in range(MAP_SIZE[1]): draw.point((x, y), get_color(heightmap[x][y], slopemap[x][y])) image.save(filename) print(filename, "saved") return def export_obj(vertices, tris, filename): file = open(filename, "w") for vertex in vertices: file.write("v " + str(vertex[0]) + " " + str(vertex[1]) + " " + str(vertex[2]) + "\n") for tri in tris: file.write("f " + str(tri[2]+1) + " " + str(tri[1]+1) + " " + str(tri[0]+1) + "\n") file.close() print(filename, "saved") return def main(): heightmap = generate_heightmap() slopemap = generate_slopemap(heightmap) vertices = generate_vertices(heightmap) edges, surfaces = generate_tris() export_obj(vertices, surfaces, "test.obj") export_norm_map(heightmap, "heightmap.png") export_norm_map(slopemap, "slopemap.png") export_texture(heightmap, slopemap, "texture.png") if __name__ == "__main__": main()
<reponame>ankushaggarwal/gpytorch import torch import sys from os.path import dirname, abspath sys.path.insert(0,dirname(dirname(dirname(abspath(__file__))))) import gpytorch import math from matplotlib import cm from matplotlib import pyplot as plt import numpy as np import numpy as np data = np.load('MV18242PL-FS.npz') F = data['F'] S = data['S'] def invariants(F): M = np.array([1,0,0]) M = M/np.linalg.norm(M) Fall = np.reshape(F,(-1,3,3)) I1,I4 = [], [] for f in Fall: C = np.dot(f.T,f) I1.append(np.trace(C)) I4.append(np.dot(M,np.dot(C,M))) return np.reshape(np.array(I1),np.shape(F)[:-2]), np.reshape(np.array(I4),np.shape(F)[:-2]) def principal_stretches(F): Fall = np.reshape(F,(-1,3,3)) l1,l2 = [], [] for f in Fall: l1.append(f[0,0]) #assumes a biaxial stretching l2.append(f[1,1]) return np.reshape(np.array(l1),np.shape(F)[:-2]), np.reshape(np.array(l2),np.shape(F)[:-2]) def stresses(S): Sall = np.reshape(S,(-1,3,3)) s11, s22 = [], [] for s in Sall: s11.append(s[0,0]) s22.append(s[1,1]) #assumes the shear stresses are zero return np.reshape(np.array(s11),np.shape(S)[:-2]), np.reshape(np.array(s22),np.shape(S)[:-2]) I1, I4 = invariants(F) lam1, lam2 = principal_stretches(F) s11, s22 = S[:,:,0,0], S[:,:,1,1] def partial_derivs(S11,S22,Lam1,Lam2): d1,d2 = [], [] A = np.zeros([2,2]) r = np.zeros(2) for (s11,s22,lam1,lam2) in zip(S11.flatten(),S22.flatten(),Lam1.flatten(),Lam2.flatten()): A[0,0] = 2*(lam1**2-1./lam1**2/lam2**2) A[1,0] = 2*(lam2**2-1./lam1**2/lam2**2) A[0,1] = 2*lam1**2 #assumes the fiber direction is the first axis r[0],r[1] = s11,s22 x = np.linalg.solve(A,r) d1.append(x[0]) d2.append(x[1]) return np.reshape(np.array(d1),np.shape(S11)), np.reshape(np.array(d2),np.shape(S11)) dWdI1, dWdI4 = partial_derivs(s11,s22,lam1,lam2) train_x=torch.vstack((torch.atleast_2d(torch.from_numpy(10*(I1.flatten()-3.))),torch.atleast_2d(torch.from_numpy(10*(I4.flatten()-1.))))).T.float() train_y=torch.vstack((torch.atleast_2d(torch.from_numpy(dWdI1.flatten())),torch.atleast_2d(torch.from_numpy(dWdI4.flatten())))).T.reshape(-1).float() train_y=(train_x**2).reshape(-1).float() ndata,ndim = train_x.shape train_index = torch.empty(ndata,ndim+1,dtype=bool) train_index[:,0]=False train_index[:,1:]=True class LinearMeanGrad(gpytorch.means.Mean): def __init__(self, input_size, batch_shape=torch.Size(), bias=True): super().__init__() self.dim = input_size self.register_parameter(name="weights", parameter=torch.nn.Parameter(torch.randn(*batch_shape, input_size, 1))) if bias: self.register_parameter(name="bias", parameter=torch.nn.Parameter(torch.randn(*batch_shape, 1))) else: self.bias = None def forward(self, x): res = x.matmul(self.weights) if self.bias is not None: res = res + self.bias dres = self.weights.expand(self.dim,x.shape[-2]).T #will not work for batches return torch.hstack((res,dres)) class GPModelWithDerivatives(gpytorch.models.ExactGP): def __init__(self, train_x, train_y, likelihood): super(GPModelWithDerivatives, self).__init__(train_x, train_y, likelihood) #self.mean_module = gpytorch.means.ConstantMeanGrad() self.mean_module = LinearMeanGrad(2,bias=False) self.base_kernel = gpytorch.kernels.RBFKernelGrad(ard_num_dims=2) self.covar_module = gpytorch.kernels.ScaleKernel(self.base_kernel) def forward(self, x, index): index = index.reshape(-1) mean_x = self.mean_module(x).reshape(-1)[index] full_kernel = self.covar_module(x) covar_x = full_kernel[..., index,:][...,:,index] return gpytorch.distributions.MultivariateNormal(mean_x, covar_x) likelihood = gpytorch.likelihoods.GaussianLikelihood() # Value + x-derivative + y-derivative model = GPModelWithDerivatives((train_x,train_index), train_y, likelihood) # this is for running the notebook in our testing framework import os smoke_test = ('CI' in os.environ) training_iter = 2 if smoke_test else 50 # Find optimal model hyperparameters model.train() likelihood.train() # Use the adam optimizer optimizer = torch.optim.Adam(model.parameters(), lr=0.05) # Includes GaussianLikelihood parameters # "Loss" for GPs - the marginal log likelihood mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model) for i in range(training_iter): optimizer.zero_grad() output = model(train_x,train_index) loss = -mll(output, train_y) loss.backward() print("Iter %d/%d - Loss: %.3f lengthscales: %.3f, %.3f noise: %.3f" % ( i + 1, training_iter, loss.item(), model.covar_module.base_kernel.lengthscale.squeeze()[0], model.covar_module.base_kernel.lengthscale.squeeze()[1], model.likelihood.noise.item() )) optimizer.step() # Set into eval mode model.eval() likelihood.eval() predictions = likelihood(model(train_x,train_index)) means = predictions.mean.detach().numpy() dWdI1p = means[::2].reshape(I1.shape) dWdI4p = means[1::2].reshape(I4.shape) #For plotting from matplotlib import pyplot as plt from mpl_toolkits.mplot3d import Axes3D # Initialize plot fig = plt.figure(figsize=(10, 6)) ax1 = fig.add_subplot(2,1, 1, projection='3d') ax2 = fig.add_subplot(2,1, 2, projection='3d') color_idx = np.linspace(0, 1, len(I1)) for (c,i1,i4,s1,s2,s1p,s2p) in zip(color_idx,I1,I4,dWdI1,dWdI4,dWdI1p,dWdI4p): #ln1 = ax1.plot(i1, i4, s1,'o',color=plt.cm.cool(c)) #ln2 = ax2.plot(i1, i4, s2,'o',color=plt.cm.cool(c)) ln3 = ax1.plot(i1, i4, s1p,'-',color=plt.cm.cool(c)) ln4 = ax2.plot(i1, i4, s2p,'-',color=plt.cm.cool(c)) ax1.elev = 60 ax2.elev = 60 ax1.set_xlabel(r'$I_1$') ax1.set_ylabel(r'$I_4$') ax1.set_zlabel(r'$\frac{\partial W}{\partial I_1}$') ax2.set_xlabel(r'$I_1$') ax2.set_ylabel(r'$I_4$') ax2.set_zlabel(r'$\frac{\partial W}{\partial I_4}$') plt.show()
import torch import torch.nn as nn from .dual import DualObject def select_input(X, epsilon, proj, norm, bounded_input, box_bounds=None): if proj is not None and norm=='l1_median' and X[0].numel() > proj: if bounded_input: return InfBallProjBounded(X,epsilon,proj) else: return InfBallProj(X,epsilon,proj) elif norm == 'l1': if box_bounds is not None: return InfBallBoxBounds(X, epsilon, box_bounds) elif bounded_input: return InfBallBounded(X, epsilon) else: return InfBall(X, epsilon) elif proj is not None and norm=='l2_normal' and X[0].numel() > proj: return L2BallProj(X,epsilon,proj) elif norm == 'l2': return L2Ball(X,epsilon) else: raise ValueError("Unknown estimation type: {}".format(norm)) class InfBall(DualObject): def __init__(self, X, epsilon): super(InfBall, self).__init__() self.epsilon = epsilon n = X[0].numel() self.nu_x = [X] self.nu_1 = [X.new(n,n)] torch.eye(n, out=self.nu_1[0]) self.nu_1[0] = self.nu_1[0].view(-1,*X.size()[1:]).unsqueeze(0) def apply(self, dual_layer): self.nu_x.append(dual_layer(*self.nu_x)) self.nu_1.append(dual_layer(*self.nu_1)) def bounds(self, network=None): if network is None: nu_1 = self.nu_1[-1] nu_x = self.nu_x[-1] else: nu_1 = network(self.nu_1[0]) nu_x = network(self.nu_x[0]) epsilon = self.epsilon l1 = nu_1.abs().sum(1) if isinstance(epsilon, torch.Tensor): while epsilon.dim() < nu_x.dim(): epsilon = epsilon.unsqueeze(1) return (nu_x - epsilon*l1, nu_x + epsilon*l1) def objective(self, *nus): epsilon = self.epsilon nu = nus[-1] nu = nu.view(nu.size(0), nu.size(1), -1) nu_x = nu.matmul(self.nu_x[0].view(self.nu_x[0].size(0),-1).unsqueeze(2)).squeeze(2) if isinstance(self.epsilon, torch.Tensor): while epsilon.dim() < nu.dim()-1: epsilon = epsilon.unsqueeze(1) l1 = epsilon*nu.abs().sum(2) return -nu_x - l1 class InfBallBounded(DualObject): def __init__(self, X, epsilon, l=0, u=1): super(InfBallBounded, self).__init__() self.epsilon = epsilon self.l = (X-epsilon).clamp(min=l).view(X.size(0), 1, -1) self.u = (X+epsilon).clamp(max=u).view(X.size(0), 1, -1) n = X[0].numel() self.nu_x = [X] self.nu_1 = [X.new(n,n)] torch.eye(n, out=self.nu_1[0]) self.nu_1[0] = self.nu_1[0].view(-1,*X.size()[1:]).unsqueeze(0) def apply(self, dual_layer): self.nu_x.append(dual_layer(*self.nu_x)) self.nu_1.append(dual_layer(*self.nu_1)) def bounds(self, network=None): if network is None: nu = self.nu_1[-1] else: nu = network(self.nu_1[0]) nu_pos = nu.clamp(min=0).view(nu.size(0), nu.size(1), -1) nu_neg = nu.clamp(max=0).view(nu.size(0), nu.size(1), -1) zu = (self.u.matmul(nu_pos) + self.l.matmul(nu_neg)).squeeze(1) zl = (self.u.matmul(nu_neg) + self.l.matmul(nu_pos)).squeeze(1) return (zl.view(zl.size(0), *nu.size()[2:]), zu.view(zu.size(0), *nu.size()[2:])) def objective(self, *nus): nu = nus[-1] nu_pos = nu.clamp(min=0).view(nu.size(0), nu.size(1), -1) nu_neg = nu.clamp(max=0).view(nu.size(0), nu.size(1), -1) u, l = self.u.unsqueeze(3).squeeze(1), self.l.unsqueeze(3).squeeze(1) return (-nu_neg.matmul(l) - nu_pos.matmul(u)).squeeze(2) class InfBallBoxBounds(DualObject): def __init__(self, X, epsilon, box_bounds): super(InfBallBoxBounds, self).__init__() self.epsilon = epsilon lo, hi = box_bounds self.l = lo.view(X.size(0), 1, -1) self.u = hi.view(X.size(0), 1, -1) n = X[0].numel() self.nu_x = [X] self.nu_1 = [X.new(n,n)] torch.eye(n, out=self.nu_1[0]) self.nu_1[0] = self.nu_1[0].view(-1,*X.size()[1:]).unsqueeze(0) def apply(self, dual_layer): self.nu_x.append(dual_layer(*self.nu_x)) self.nu_1.append(dual_layer(*self.nu_1)) def bounds(self, network=None): if network is None: nu = self.nu_1[-1] else: nu = network(self.nu_1[0]) nu_pos = nu.clamp(min=0).view(nu.size(0), nu.size(1), -1) nu_neg = nu.clamp(max=0).view(nu.size(0), nu.size(1), -1) zu = (self.u.matmul(nu_pos) + self.l.matmul(nu_neg)).squeeze(1) zl = (self.u.matmul(nu_neg) + self.l.matmul(nu_pos)).squeeze(1) return (zl.view(zl.size(0), *nu.size()[2:]), zu.view(zu.size(0), *nu.size()[2:])) def objective(self, *nus): nu = nus[-1] nu_pos = nu.clamp(min=0).view(nu.size(0), nu.size(1), -1) nu_neg = nu.clamp(max=0).view(nu.size(0), nu.size(1), -1) u, l = self.u.unsqueeze(3).squeeze(1), self.l.unsqueeze(3).squeeze(1) return (-nu_neg.matmul(l) - nu_pos.matmul(u)).squeeze(2) class InfBallProj(InfBall): def __init__(self, X, epsilon, k): DualObject.__init__(self) self.epsilon = epsilon n = X[0].numel() self.nu_x = [X] self.nu = [X.new(1,k,*X.size()[1:]).cauchy_()] def apply(self, dual_layer): self.nu_x.append(dual_layer(*self.nu_x)) self.nu.append(dual_layer(*self.nu)) def bounds(self, network=None): if network is None: nu = self.nu[-1] nu_x = self.nu_x[-1] else: nu = network(self.nu[0]) nu_x = network(self.nu_x[0]) l1 = torch.median(self.nu[-1].abs(), 1)[0] return (nu_x - self.epsilon*l1, nu_x + self.epsilon*l1) class InfBallProjBounded(InfBallProj): def __init__(self, X, epsilon, k, l=0, u=1): self.epsilon = epsilon self.nu_one_l = [(X-epsilon).clamp(min=l)] self.nu_one_u = [(X+epsilon).clamp(max=u)] self.nu_x = [X] self.l = self.nu_one_l[-1].view(X.size(0), 1, -1) self.u = self.nu_one_u[-1].view(X.size(0), 1, -1) n = X[0].numel() R = X.new(1,k,*X.size()[1:]).cauchy_() self.nu_l = [R * self.nu_one_l[-1].unsqueeze(1)] self.nu_u = [R * self.nu_one_u[-1].unsqueeze(1)] def apply(self, dual_layer): self.nu_l.append(dual_layer(*self.nu_l)) self.nu_one_l.append(dual_layer(*self.nu_one_l)) self.nu_u.append(dual_layer(*self.nu_u)) self.nu_one_u.append(dual_layer(*self.nu_one_u)) def bounds(self, network=None): if network is None: nu_u = self.nu_u[-1] nu_one_u = self.nu_one_u[-1] nu_l = self.nu_l[-1] nu_one_l = self.nu_one_l[-1] else: nu_u = network(self.nu_u[0]) nu_one_u = network(self.nu_one_u[0]) nu_l = network(self.nu_l[0]) nu_one_l = network(self.nu_one_l[0]) nu_l1_u = torch.median(nu_u.abs(),1)[0] nu_pos_u = (nu_l1_u + nu_one_u)/2 nu_neg_u = (-nu_l1_u + nu_one_u)/2 nu_l1_l = torch.median(nu_l.abs(),1)[0] nu_pos_l = (nu_l1_l + nu_one_l)/2 nu_neg_l = (-nu_l1_l + nu_one_l)/2 zu = nu_pos_u + nu_neg_l zl = nu_neg_u + nu_pos_l return zl,zu # L2 balls class L2Ball(DualObject): def __init__(self, X, epsilon): super(L2Ball, self).__init__() self.epsilon = epsilon n = X[0].numel() self.nu_x = [X] self.nu_1 = [X.new(n,n)] torch.eye(n, out=self.nu_1[0]) self.nu_1[0] = self.nu_1[0].view(-1,*X.size()[1:]).unsqueeze(0) def apply(self, dual_layer): self.nu_x.append(dual_layer(*self.nu_x)) self.nu_1.append(dual_layer(*self.nu_1)) def bounds(self, network=None): if network is None: nu_1 = self.nu_1[-1] nu_x = self.nu_x[-1] else: nu_1 = network(self.nu_1[0]) nu_x = network(self.nu_x[0]) epsilon = self.epsilon l2 = nu_1.norm(2, 1) if isinstance(epsilon, torch.Tensor): while epsilon.dim() < nu_x.dim(): epsilon = epsilon.unsqueeze(1) return (nu_x - epsilon*l2, nu_x + epsilon*l2) def objective(self, *nus): epsilon = self.epsilon nu = nus[-1] nu = nu.view(nu.size(0), nu.size(1), -1) nu_x = nu.matmul(self.nu_x[0].view(self.nu_x[0].size(0),-1).unsqueeze(2)).squeeze(2) if isinstance(self.epsilon, torch.Tensor): while epsilon.dim() < nu.dim()-1: epsilon = epsilon.unsqueeze(1) l2 = nu.norm(2,2) return -nu_x - epsilon * l2 class L2BallProj(L2Ball): def __init__(self, X, epsilon, k): DualObject.__init__(self) self.epsilon = epsilon n = X[0].numel() self.nu_x = [X] self.nu = [X.new(1,k,*X.size()[1:]).normal_()] def apply(self, dual_layer): self.nu_x.append(dual_layer(*self.nu_x)) self.nu.append(dual_layer(*self.nu)) def bounds(self, network=None): if network is None: nu = self.nu[-1] nu_x = self.nu_x[-1] else: nu = network(self.nu[0]) nu_x = network(self.nu_x[0]) k = nu.size(1) l2 = nu.norm(2, 1)/(k**0.5) return (nu_x - self.epsilon*l2, nu_x + self.epsilon*l2)
<gh_stars>1-10 # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Copyright (c) 2020, Xiaomi CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Optional, Union import torch from omegaconf import DictConfig from nemo.collections.asr.parts.k2.autograd import sparse_abs from nemo.collections.asr.parts.k2.classes import GraphIntersectDenseConfig from nemo.collections.asr.parts.k2.utils import ( create_sparse_wrapped, create_supervision, get_tot_objf_and_finite_mask, load_graph, make_blank_first, prep_padded_densefsavec, shift_labels_inpl, ) from nemo.utils import logging # use k2 import guard # fmt: off from nemo.core.utils.k2_utils import k2_import_guard # isort:skip k2_import_guard() import k2 # isort:skip # fmt: on class MAPLoss(torch.nn.Module): """ Maximum a Posteriori Probability criterion. It is implemented as Lattice-Free Maximum Mutual Information (LF-MMI) and LF-boosted-MMI (LF-bMMI) losses. Based on https://github.com/k2-fsa/snowfall/blob/master/snowfall/objectives/mmi.py cfg takes precedence over all optional parameters We keep explicit parameter setting to be able to create an instance without the need of a config. """ def __init__( self, num_classes: int, blank: int, reduction: str, cfg: Optional[DictConfig] = None, topo_type: str = "default", topo_with_self_loops: bool = True, loss_type: str = "mmi", token_lm: Optional[Union['k2.Fsa', str]] = None, intersect_pruned: bool = False, intersect_conf: GraphIntersectDenseConfig = GraphIntersectDenseConfig(), boost_coeff: float = 0.0, ): # use k2 import guard k2_import_guard() super().__init__() if cfg is not None: topo_type = cfg.get("topo_type", topo_type) topo_with_self_loops = cfg.get("topo_with_self_loops", topo_with_self_loops) loss_type = cfg.get("loss_type", loss_type) token_lm = cfg.get("token_lm", token_lm) intersect_pruned = cfg.get("intersect_pruned", intersect_pruned) intersect_conf = cfg.get("intersect_conf", intersect_conf) boost_coeff = cfg.get("boost_coeff", boost_coeff) self.num_classes = num_classes self.blank = blank self.reduction = reduction self.loss_type = loss_type self.boost_coeff = boost_coeff self.intersect_calc_scores = ( self._intersect_calc_scores_mmi_pruned if intersect_pruned else self._intersect_calc_scores_mmi_exact ) self.intersect_conf = intersect_conf self.topo_type = topo_type self.topo_with_self_loops = topo_with_self_loops self.pad_fsavec = topo_type == "compact" self.graph_compiler = None if token_lm is None: logging.warning( f"""token_lm is empty. Trainable token_lm is not supported yet. Please call .update_graph(token_lm) before using.""" ) else: self.lm_graph = load_graph(token_lm) if isinstance(token_lm, str) else token_lm if self.lm_graph is None: raise ValueError(f"""lm_graph is empty.""") else: self.update_graph(self.lm_graph) def update_graph(self, graph: 'k2.Fsa'): self.lm_graph = graph lm_graph = self.lm_graph.clone() if hasattr(lm_graph, "aux_labels"): delattr(lm_graph, "aux_labels") labels = lm_graph.labels if labels.max() != self.num_classes - 1: raise ValueError(f"lm_graph is not compatible with the num_classes: {labels.unique()}, {self.num_classes}") if self.pad_fsavec: shift_labels_inpl([lm_graph], 1) if self.loss_type == "mmi": from nemo.collections.asr.parts.k2.graph_compilers import MmiGraphCompiler as compiler else: raise ValueError(f"Invalid value of `loss_type`: {self.loss_type}.") self.graph_compiler = compiler(self.num_classes, self.topo_type, self.topo_with_self_loops, aux_graph=lm_graph) def _intersect_calc_scores_mmi_exact( self, dense_fsa_vec: k2.DenseFsaVec, num_graphs: 'k2.Fsa', den_graph: 'k2.Fsa', return_lats: bool = True, ): device = dense_fsa_vec.device assert device == num_graphs.device and device == den_graph.device num_fsas = num_graphs.shape[0] assert dense_fsa_vec.dim0() == num_fsas den_graph = den_graph.clone() num_graphs = num_graphs.clone() num_den_graphs = k2.cat([num_graphs, den_graph]) # NOTE: The a_to_b_map in k2.intersect_dense must be sorted # so the following reorders num_den_graphs. # [0, 1, 2, ... ] num_graphs_indexes = torch.arange(num_fsas, dtype=torch.int32) # [num_fsas, num_fsas, num_fsas, ... ] den_graph_indexes = torch.tensor([num_fsas] * num_fsas, dtype=torch.int32) # [0, num_fsas, 1, num_fsas, 2, num_fsas, ... ] num_den_graphs_indexes = torch.stack([num_graphs_indexes, den_graph_indexes]).t().reshape(-1).to(device) num_den_reordered_graphs = k2.index_fsa(num_den_graphs, num_den_graphs_indexes) # [[0, 1, 2, ...]] a_to_b_map = torch.arange(num_fsas, dtype=torch.int32).reshape(1, -1) # [[0, 1, 2, ...]] -> [0, 0, 1, 1, 2, 2, ... ] a_to_b_map = a_to_b_map.repeat(2, 1).t().reshape(-1).to(device) num_den_lats = k2.intersect_dense( a_fsas=num_den_reordered_graphs, b_fsas=dense_fsa_vec, output_beam=self.intersect_conf.output_beam, a_to_b_map=a_to_b_map, seqframe_idx_name="seqframe_idx" if return_lats else None, ) num_den_tot_scores = num_den_lats.get_tot_scores(log_semiring=True, use_double_scores=True) num_tot_scores = num_den_tot_scores[::2] den_tot_scores = num_den_tot_scores[1::2] if return_lats: lat_slice = torch.arange(num_fsas, dtype=torch.int32).to(device) * 2 return ( num_tot_scores, den_tot_scores, k2.index_fsa(num_den_lats, lat_slice), k2.index_fsa(num_den_lats, lat_slice + 1), ) else: return num_tot_scores, den_tot_scores, None, None def _intersect_calc_scores_mmi_pruned( self, dense_fsa_vec: k2.DenseFsaVec, num_graphs: 'k2.Fsa', den_graph: 'k2.Fsa', return_lats: bool = True, ): device = dense_fsa_vec.device assert device == num_graphs.device and device == den_graph.device num_fsas = num_graphs.shape[0] assert dense_fsa_vec.dim0() == num_fsas num_lats = k2.intersect_dense( a_fsas=num_graphs, b_fsas=dense_fsa_vec, output_beam=self.intersect_conf.output_beam, seqframe_idx_name="seqframe_idx" if return_lats else None, ) den_lats = k2.intersect_dense_pruned( a_fsas=den_graph, b_fsas=dense_fsa_vec, search_beam=self.intersect_conf.search_beam, output_beam=self.intersect_conf.output_beam, min_active_states=self.intersect_conf.min_active_states, max_active_states=self.intersect_conf.max_active_states, seqframe_idx_name="seqframe_idx" if return_lats else None, ) # use_double_scores=True does matter # since otherwise it sometimes makes rounding errors num_tot_scores = num_lats.get_tot_scores(log_semiring=True, use_double_scores=True) den_tot_scores = den_lats.get_tot_scores(log_semiring=True, use_double_scores=True) if return_lats: return num_tot_scores, den_tot_scores, num_lats, den_lats else: return num_tot_scores, den_tot_scores, None, None def forward( self, log_probs: torch.Tensor, targets: torch.Tensor, input_lengths: torch.Tensor, target_lengths: torch.Tensor, ) -> torch.Tensor: assert self.graph_compiler is not None boosted = self.boost_coeff != 0.0 if self.blank != 0: # rearrange log_probs to put blank at the first place # and shift targets to emulate blank = 0 log_probs, targets = make_blank_first(self.blank, log_probs, targets) supervisions, order = create_supervision(input_lengths) order = order.long() targets = targets[order] target_lengths = target_lengths[order] if log_probs.device != self.graph_compiler.device: self.graph_compiler.to(log_probs.device) num_graphs, den_graph = self.graph_compiler.compile( targets + 1 if self.pad_fsavec else targets, target_lengths ) dense_fsa_vec = ( prep_padded_densefsavec(log_probs, supervisions) if self.pad_fsavec else k2.DenseFsaVec(log_probs, supervisions) ) num_tot_scores, den_tot_scores, num_lats, den_lats = self.intersect_calc_scores( dense_fsa_vec, num_graphs, den_graph, boosted ) tot_scores = num_tot_scores - den_tot_scores mmi_tot_scores, mmi_valid_mask = get_tot_objf_and_finite_mask(tot_scores, self.reduction) if boosted: assert num_lats is not None and den_lats is not None size = ( dense_fsa_vec.dim0(), dense_fsa_vec.scores.shape[0], dense_fsa_vec.scores.shape[1] - 1, ) row_ids = dense_fsa_vec.dense_fsa_vec.shape().row_ids(1) num_sparse = create_sparse_wrapped( indices=[k2.index_select(row_ids, num_lats.seqframe_idx), num_lats.seqframe_idx, num_lats.phones,], values=num_lats.get_arc_post(False, True).exp(), size=size, min_col_index=0, ) den_sparse = create_sparse_wrapped( indices=[k2.index_select(row_ids, den_lats.seqframe_idx), den_lats.seqframe_idx, den_lats.phones,], values=den_lats.get_arc_post(False, True).exp(), size=size, min_col_index=0, ) # NOTE: Due to limited support of PyTorch's autograd for sparse tensors, # we cannot use (num_sparse - den_sparse) here # TODO (alaptev): propose sparse_abs to k2 acc_loss = torch.sparse.sum(sparse_abs((num_sparse + (-den_sparse)).coalesce()), (1, 2)).to_dense() acc_tot_scores, acc_valid_mask = get_tot_objf_and_finite_mask(acc_loss, self.reduction) valid_mask = mmi_valid_mask & acc_valid_mask total_loss = self.boost_coeff * acc_tot_scores[valid_mask] - mmi_tot_scores[valid_mask] else: valid_mask = mmi_valid_mask total_loss = -mmi_tot_scores[mmi_valid_mask] return total_loss, valid_mask
from unittest import TestCase, TestLoader, TextTestRunner from pathlib import Path import sys from sndpgen import SndpGraph, Timer, parse_args_sndp_gen, generate_command class TestSndpGraph(TestCase): @classmethod def setUpClass(cls): SndpGraph.DEBUG = True def test_init(self): num_locations = 5 num_products = 5 num_scen = 10 graph = SndpGraph('instance_name', 5, 5, 10, 1) self.assertEqual(len(graph.get_scenarios()),num_scen) self.assertEqual(len(graph.get_materials()), num_products-1) # last product in end product self.assertEqual(len(graph.get_products()), num_products) self.assertEqual(len(graph.get_locations()), num_locations) def test_regenerate_stochastic_data(self): graph = SndpGraph('instance_name', 5, 5, 10, 1) new_num_scen = 5 graph.regenerate_stochastic_data(new_num_scen) self.assertEqual(len(graph.get_scenarios()), new_num_scen) def test_visualize(self): graph = SndpGraph('instance_name', 20, 5, 20, 1) graph.visualize() def test_data_as_dict(self): num_locations = 5 num_products = 3 num_scen = 2 graph = SndpGraph('instance_name', num_locations, num_products, num_scen, 2) data = graph.data_as_dict self.assertEqual(data['NrOfLocations'], num_locations) self.assertEqual(data['NrOfProducts'], num_products) self.assertEqual(data['NrOfScen'], num_scen) # data below might change if we modify the class variables of SndpGraph self.assertListEqual(data['MaterialReq'], [{'material': 1, 'value': 2}, {'material': 2, 'value': 3}]) self.assertListEqual(data['Prob'], [{'SCEN': 1, 'value': 0.5}, {'SCEN': 2, 'value': 0.5}]) self.assertListEqual(data['Demand'], [{'SCEN': 1, 'value': 5673}, {'SCEN': 2, 'value': 7295}]) def test_adjust_sales_price(self): num_locations = 10 num_products = 5 num_scen = 3 graph = SndpGraph('instance_name', num_locations, num_products, num_scen, 2) init_sales_price = graph.sales_price graph.adjust_sales_price() self.assertLessEqual(graph.sales_price, init_sales_price) @classmethod def tearDownClass(cls): for file in Path().glob("instance_name*"): file.unlink() class TestCommandLineSndpGen(TestCase): @classmethod def setUpClass(cls): SndpGraph.DEBUG = True def test_parse_args(self): filename = 'param.yaml' parsed = parse_args_sndp_gen(['--yaml', filename]) self.assertEqual(parsed.yaml, filename) def test_parse_args_default_yaml(self): parsed = parse_args_sndp_gen([]) self.assertEqual(parsed.yaml, 'param.yaml') def test_command_line(self): Timer('test_command_line').start() filename = 'param.yaml' sys.argv = sys.argv + ['--yaml', filename] self.assertTrue(generate_command()) Timer('test_command_line').pause() Timer.report() def test_command_default_yaml(self): self.assertTrue(generate_command()) def test_command_wrong_yaml(self): filename = 'param_wrong.yaml' sys.argv = sys.argv + ['--yaml', filename] self.assertFalse(generate_command()) @classmethod def tearDownClass(cls): for file in Path().glob("SNDP_*"): file.unlink() try: from sndpgen.sndp_model import SndpModel except ImportError: pass else: class TestSndpModel(TestCase): @classmethod def setUpClass(cls): cls.model_path = Path(f'10_5_0_1.mpl') def test_adjust_sales_price(self): original_sndp_model = SndpModel(self.model_path) sndp_model_path = Path(f'SNDP_10_5_0_1_temp.mpl') original_sndp_model.export(sndp_model_path) # we do not want to modify the original model sndp_model = SndpModel(sndp_model_path) init_sales_price = sndp_model.data_as_dict['SalesPrice'] init_num_open_locations = len(sndp_model.solution_open_production) sndp_model.adjust_sales_price() adjusted_sales_price = sndp_model.data_as_dict['SalesPrice'] adjusted_num_open_locations = len(sndp_model.solution_open_production) self.assertLessEqual(adjusted_sales_price, init_sales_price) self.assertLessEqual(init_num_open_locations, adjusted_num_open_locations) @classmethod def tearDownClass(cls): for file in Path().glob("SNDP_*"): file.unlink() if __name__ == '__main__': loader = TestLoader() suite = loader.discover('') runner = TextTestRunner(verbosity=2) runner.run(suite)
<gh_stars>0 import time import numpy as np import scipy import scipy.sparse import scipy.sparse.linalg from copy import copy from thimbles.sqlaimports import * from thimbles.thimblesdb import ThimblesTable, Base from sqlalchemy.orm.collections import attribute_mapped_collection from sqlalchemy.orm.collections import collection from functools import reduce from .associations import InformedContexts, NamedContexts mult_func = lambda x, y: x*y def flat_size(shape_tup): if shape_tup == tuple(): return 1 return reduce(mult_func, shape_tup) class Parameter(ThimblesTable, Base): parameter_class = Column(String) __mapper_args__={ "polymorphic_identity":"parameter", "polymorphic_on": parameter_class } _value = None models = relationship( "InputAlias", collection_class = InformedContexts, ) distributions = relationship( "DistributionAlias", collection_class = NamedContexts, ) _setting_callbacks = None _invalidation_callbacks = None def __init__(self, value=None): self._value = value def add_callback(self, cb_name, cb_function, cb_type="invalid"): """add a function to be called whenever this parameters value is set or invalidated depending on cb_type argument. """ if cb_type == "set": if self._setting_callbacks is None: self._setting_callbacks = {} self._setting_callbacks[cb_name] = cb_function elif cb_type == "invalid": if self._invalidation_callbacks is None: self._invalidation_callbacks = {} self._invalidation_callbacks[cb_name] = cb_function else: raise ValueError("cb_type must be one of {}".format(["set" "invalid"])) def fire_upstream(self): """fire the model immediately upstream from this parameter. Often this will require also causing the models upstream from it to fire as well and so on causing a cascade. """ m_models = self.mapped_models if len(m_models) >= 1: if len(m_models) > 1: print("warning parameter value regeneration is non-unique consider changing the model hierarchy to use cached parameter values.") mod = m_models[0] mod.fire() #should populate our self._value attribute def invalidate_downstream(self): """run through all parameters downstream of this one marking them as invalid, so that subsequent calls asking for their value will trigger an update cascade. """ parameter_front = [mod.output_p for mod in self.models if (not mod.output_p is None)] while len(parameter_front) > 0: param = parameter_front.pop(0) param.invalidate() #find all the downstream parameters for mod in param.models: out_p = mod.output_p #check that the model actually maps to something. if not out_p is None: #if parameter is not already invalid add it to the queue. if not out_p.invalid(): parameter_front.append(out_p) @property def value(self): return self.get() @value.setter def value(self, value): self.set(value,) def invalid(self,): return self._value is None def execute_invalidation_callbacks(self): if self._invalidation_callbacks is None: self._invalidation_callbacks = {} for cb_name in self._invalidation_callbacks: cb_func = self._invalidation_callbacks[cb_name] cb_func() def invalidate(self,): if not self.invalid(): self._value = None self.execute_invalidation_callbacks() def get(self,): if self.invalid(): self.fire_upstream() return self._value def execute_setting_callbacks(self): if self._setting_callbacks is None: self._setting_callbacks = {} for cb_name in self._setting_callbacks: cb_func = self._setting_callbacks[cb_name] cb_func(self._value) def set(self, value): self.invalidate_downstream() self._value = value self.execute_setting_callbacks() @property def shape(self): return np.asarray(self.get()).shape #how to make a parameter subclass #class classname(Parameter): # _id = Column(Integer, ForeignKey("Parameter._id"), primary_key=True) # _value = Column(Float) # __mapper_args__={ # "polymorphic_identity": "classname" # }
<reponame>cdlaimin/CoolQBot<filename>src/plugins/morning/__init__.py """ 每日早安插件 """ import nonebot from nonebot import get_bot from nonebot.adapters import Bot from nonebot.adapters.cqhttp.event import GroupMessageEvent from nonebot.adapters.cqhttp.permission import GROUP from nonebot.log import logger from nonebot.plugin import on_command from nonebot_plugin_apscheduler import scheduler from src.utils.helpers import strtobool from .config import plugin_config from .data import HOLIDAYS_DATA, get_first_connect_message, get_moring_message driver = nonebot.get_driver() # region 启动问候 @driver.on_bot_connect async def hello_on_connect(bot: Bot) -> None: """启动时发送问候""" hello_str = get_first_connect_message() for group_id in plugin_config.hello_group_id: await bot.send_msg(message_type="group", group_id=group_id, message=hello_str) logger.info("发送首次启动的问候") hello_cmd = on_command("hello", aliases={"问候"}, permission=GROUP) hello_cmd.__doc__ = """ hello 问候 启动问候 开启时会在每天机器人第一次启动时发送问候 查看当前群是否开启启动问候 /hello 开启启动问候功能 /hello on 关闭启动问候功能 /hello off """ @hello_cmd.handle() async def hello_handle(bot: Bot, event: GroupMessageEvent): args = str(event.message).strip() group_id = event.group_id if args and group_id: if strtobool(args): plugin_config.hello_group_id += [group_id] await hello_cmd.finish("已在本群开启启动问候功能") else: plugin_config.hello_group_id = [ n for n in plugin_config.hello_group_id if n != group_id ] await hello_cmd.finish("已在本群关闭启动问候功能") else: if group_id in plugin_config.hello_group_id: await hello_cmd.finish("启动问候功能开启中") else: await hello_cmd.finish("启动问候功能关闭中") # endregion # region 每日早安 @scheduler.scheduled_job( "cron", hour=plugin_config.morning_hour, minute=plugin_config.morning_minute, second=plugin_config.morning_second, id="morning", ) async def morning(): """早安""" hello_str = await get_moring_message() for group_id in plugin_config.morning_group_id: await get_bot().send_msg( message_type="group", group_id=group_id, message=hello_str, ) logger.info("发送早安信息") morning_cmd = on_command("morning", aliases={"早安"}, permission=GROUP) morning_cmd.__doc__ = """ morning 早安 每日早安 开启时会在每天早晨发送早安信息 查看当前群是否开启每日早安功能 /morning 开启每日早安功能 /morning on 关闭每日早安功能 /morning off 更新节假日数据 /morning update 获取今天的问好 /morning today """ @morning_cmd.handle() async def morning_handle(bot: Bot, event: GroupMessageEvent): args = str(event.message).strip() group_id = event.group_id if args == "today": await morning_cmd.finish(await get_moring_message()) if args == "update": await HOLIDAYS_DATA.update() await morning_cmd.finish("节假日数据更新成功") if args and group_id: if strtobool(args): plugin_config.morning_group_id += [group_id] await morning_cmd.finish("已在本群开启每日早安功能") else: plugin_config.morning_group_id = [ n for n in plugin_config.morning_group_id if n != group_id ] await morning_cmd.finish("已在本群关闭每日早安功能") else: if group_id in plugin_config.morning_group_id: await morning_cmd.finish("每日早安功能开启中") else: await morning_cmd.finish("每日早安功能关闭中") # endregion
import MySQLdb import suds from suds.client import Client from suds.xsd.doctor import Import, ImportDoctor from suds.plugin import MessagePlugin import traceback class MagentoApiMessagePlugin(MessagePlugin): def marshalled(self, context): body = context.envelope.getChild("Body") call = context.envelope.childAtPath("Body/call") if call: resourcePath = call.getChild("resourcePath") if resourcePath is not None and (str(resourcePath.getText()) == 'sales_order_shipment.create' or str(resourcePath.getText()) == 'sales_order_invoice.create'): args = call.getChild("args") if args: item = args.getChild("item") if item: item.set("xsi:type","http://xml.apache.org/xml-soap:Map") return context class Adaptor(object): """Adaptor contain MySQL cursor object. """ def __init__(self, mySQLConn=None): self._mySQLConn = mySQLConn self._mySQLCursor = self._mySQLConn.cursor(MySQLdb.cursors.DictCursor) @property def mySQLCursor(self): """MySQL Server cursor object. """ return self._mySQLCursor def disconnect(self): self._mySQLConn.close() def rollback(self): self._mySQLConn.rollback() def commit(self): self._mySQLConn.commit() class ApiAdaptor(object): """Adaptor contain API connection """ def __init__(self,apiConn=None,apiSessionId=None): self._apiConn = apiConn self._apiSessionId = apiSessionId @property def apiConn(self): return self._apiConn @property def apiSessionId(self): return self._apiSessionId def disconnect(self): if self._apiSessionId is not None and self._apiConn is not None: try: result = self._apiConn.service.endSession(self._apiSessionId) self.logger.info("Logout Magento 1 API: sessionId = {0}".format(self._apiSessionId)) except Exception as e: self.logger.exception("Failed to logout Magento 1 API with error: {0}".format(e)) raise class Mage1Connector(object): """Magento 1 connection with functions. """ GETPRODUCTIDBYSKUSQL = """SELECT distinct entity_id FROM catalog_product_entity WHERE sku = %s;""" ENTITYMETADATASQL = """ SELECT eet.entity_type_id, eas.attribute_set_id FROM eav_entity_type eet, eav_attribute_set eas WHERE eet.entity_type_id = eas.entity_type_id AND eet.entity_type_code = %s AND eas.attribute_set_name = %s;""" ATTRIBUTEMETADATASQL = """ SELECT DISTINCT t1.attribute_id, t2.entity_type_id, t1.backend_type, t1.frontend_input FROM eav_attribute t1, eav_entity_type t2 WHERE t1.entity_type_id = t2.entity_type_id AND t1.attribute_code = %s AND t2.entity_type_code = %s;""" ISENTITYEXITSQL = """SELECT count(*) as count FROM {0}_entity WHERE entity_id = %s;""" ISATTRIBUTEVALUEEXITSQL = """ SELECT count(*) as count FROM {0}_entity_{1} WHERE attribute_id = %s AND store_id = %s AND {2} = %s;""" REPLACEATTRIBUTEVALUESQL = """REPLACE INTO {0}_entity_{1} ({2}) values ({3});""" UPDATEENTITYUPDATEDATSQL = """UPDATE {0}_entity SET updated_at = UTC_TIMESTAMP() WHERE entity_id = %s;""" GETOPTIONIDSQL = """ SELECT t2.option_id FROM eav_attribute_option t1, eav_attribute_option_value t2 WHERE t1.option_id = t2.option_id AND t1.attribute_id = %s AND t2.value = %s AND t2.store_id = %s;""" INSERTCATALOGPRODUCTENTITYEESQL = """ INSERT INTO catalog_product_entity (entity_id, created_in, updated_in, attribute_set_id, type_id, sku, has_options, required_options, created_at, updated_at) VALUES(0, 1, 2147483647, %s, %s, %s, 0, 0, UTC_TIMESTAMP(), UTC_TIMESTAMP());""" INSERTCATALOGPRODUCTENTITYSQL = """ INSERT INTO catalog_product_entity (attribute_set_id, type_id, sku, has_options, required_options, created_at, updated_at) VALUES(%s, %s, %s, 0, 0, UTC_TIMESTAMP(), UTC_TIMESTAMP());""" UPDATECATALOGPRODUCTSQL = """ UPDATE catalog_product_entity SET attribute_set_id = %s, type_id = %s, updated_at = UTC_TIMESTAMP() WHERE {0} = %s;""" INSERTEAVATTRIBUTEOPTIONSQL = """INSERT INTO eav_attribute_option (attribute_id) VALUES (%s);""" OPTIONVALUEEXISTSQL = """ SELECT COUNT(*) as cnt FROM eav_attribute_option_value WHERE option_id = %s AND store_id = %s;""" INSERTOPTIONVALUESQL = """INSERT INTO eav_attribute_option_value (option_id, store_id, value) VALUES (%s, %s, %s);""" UPDATEOPTIONVALUESQL = """UPDATE eav_attribute_option_value SET value = %s WHERE option_id = %s AND store_id = %s;""" GETATTRIBUTESBYENTITYTYPEANDATTRIBUTESETSQL = """ SELECT a.entity_type_id, b.entity_type_code, d.attribute_set_id, d.attribute_set_name, a.attribute_id, a.attribute_code, a.backend_type, a.frontend_input, a.frontend_label, a.is_required, a.is_user_defined FROM eav_attribute a INNER JOIN eav_entity_attribute c on (a.attribute_id = c.attribute_id and a.entity_type_id = c.entity_type_id) INNER JOIN eav_attribute_set d on (c.attribute_set_id = d.attribute_set_id) INNER JOIN eav_entity_type b on (a.entity_type_id = b.entity_type_id) WHERE b.entity_type_code = %s and d.attribute_set_name = %s """ GETATTRIBUTESBYENTITYTYPESQL = """ SELECT a.entity_type_id, b.entity_type_code, a.attribute_id, a.attribute_code, a.backend_type, a.frontend_input, a.frontend_label, a.is_required, a.is_user_defined FROM eav_attribute a INNER JOIN eav_entity_type b on (a.entity_type_id = b.entity_type_id) WHERE b.entity_type_code = %s """ GETATTRIBUTEVALUESQL = "SELECT value FROM {0}_entity_{1} WHERE attribute_id = %s AND entity_id = %s" GETATTRIBUTEOPTIONVALUESQL = """ SELECT t3.value FROM eav_attribute_option t2, eav_attribute_option_value t3 WHERE t2.option_id = t3.option_id AND t2.attribute_id = %s AND t3.option_id = %s AND t3.store_id = %s """ EXPORTPRODUCTSSQL = """ SELECT a.*, b.attribute_set_name FROM catalog_product_entity a INNER JOIN eav_attribute_set b ON a.attribute_set_id = b.attribute_set_id WHERE updated_at >= %s AND b.attribute_set_name LIKE %s """ EXPORTPRODUCTSCOUNTSQL = """ SELECT count(*) AS total FROM catalog_product_entity a INNER JOIN eav_attribute_set b ON a.attribute_set_id = b.attribute_set_id WHERE updated_at >= %s AND b.attribute_set_name LIKE %s """ EXPORTSTOCKSQL = """ SELECT a.item_id, b.sku, 'admin' as website_code, a.qty, a.is_in_stock FROM cataloginventory_stock_item a INNER JOIN catalog_product_entity b on a.product_id = b.entity_id WHERE b.updated_at >= %s """ EXPORTSTOCKCOUNTSQL = """ SELECT count(*) AS total FROM cataloginventory_stock_item a INNER JOIN catalog_product_entity b on a.product_id = b.entity_id WHERE b.updated_at >= %s """ ISCANINVOICESQL = """ SELECT entity_id, increment_id, base_grand_total, base_total_invoiced FROM sales_flat_order WHERE increment_id = %s """ GETINVOICEINCIDSQL = """ SELECT t1.increment_id FROM sales_flat_order t0, sales_flat_invoice t1 WHERE t0.entity_id = t1.order_id AND t0.increment_id = %s """ GETORDERLINEITEMBYSKUSQL = """ SELECT a.item_id FROM sales_flat_order_item a, sales_flat_order b WHERE a.parent_item_id is null AND a.order_id = b.entity_id AND a.sku = %s AND b.increment_id = %s """ EXPORTMEDIAIMAGESSQL = """ SELECT t0.sku, CONCAT('{0}', t1.value) as 'value', %s as 'type' FROM catalog_product_entity t0, catalog_product_entity_varchar t1, eav_attribute t2 WHERE t0.entity_id = t1.entity_id AND t1.attribute_id = t2.attribute_id AND t2.attribute_code = %s AND t0.updated_at >= %s """ EXPORTMEDIAGALLERYSQL = """ SELECT t0.sku, CONCAT('{0}', t1.value) as 'value', t2.store_id, t2.position, t2.label, 'mage1' as 'media_source', 'media_gallery' as 'type' FROM catalog_product_entity t0, catalog_product_entity_media_gallery t1, catalog_product_entity_media_gallery_value t2 WHERE t0.entity_id = t1.entity_id AND t1.value_id = t2.value_id AND t0.updated_at >= %s """ def __init__(self, setting=None, logger=None): self.setting = setting self.logger = logger self._adaptor = None self._apiAdaptor = None def connect(self,type=None): """Initiate the connect with MySQL server or API connection. """ if type == 'API': suds.bindings.binding.envns = ('SOAP-ENV', 'http://schemas.xmlsoap.org/soap/envelope/') imp = Import('http://schemas.xmlsoap.org/soap/encoding/') imp.filter.add('urn:Magento') doctor = ImportDoctor(imp) if 'MAGE1APIHTTPUSERNAME' in self.setting: mageApiConn = Client( self.setting['MAGE1WSDL'], doctor=doctor, plugins=[MagentoApiMessagePlugin()], username = self.setting['MAGE1APIHTTPUSERNAME'], password = self.setting['<PASSWORD>']) else : mageApiConn = Client( self.setting['MAGE1WSDL'], doctor=doctor, plugins=[MagentoApiMessagePlugin()]) apiSessionId = mageApiConn.service.login(self.setting['MAGE1APIUSER'], self.setting['MAGE1APIKEY']) return ApiAdaptor(mageApiConn,apiSessionId) elif type == "CURSOR": mySQLConn = MySQLdb.connect(user=self.setting['MAGE1DBUSERNAME'], passwd=self.setting['<PASSWORD>'], db=self.setting['MAGE1DB'], host=self.setting['MAGE1DBSERVER'], port=self.setting['MAGE1DBPORT'], charset="utf8", use_unicode=False) log = "Open Mage1 DB connection" self.logger.info(log) return Adaptor(mySQLConn) else: return None @property def adaptor(self): self._adaptor = self.connect(type="CURSOR") if self._adaptor is None else self._adaptor return self._adaptor @property def apiAdaptor(self): self._apiAdaptor = self.connect(type="API") if self._apiAdaptor is None else self._apiAdaptor return self._apiAdaptor def getEntityMetaData(self, entityTypeCode='catalog_product', attributeSet='Default'): self.adaptor.mySQLCursor.execute(self.ENTITYMETADATASQL, [entityTypeCode, attributeSet]) entityMetadata = self.adaptor.mySQLCursor.fetchone() if entityMetadata is not None: return entityMetadata else: log = "attribute_set/entity_type_code: {0}/{1} not existed".format(attributeSet, entityTypeCode) raise Exception(log) def getAttributeMetadata(self, attributeCode, entityTypeCode): self.adaptor.mySQLCursor.execute(self.ATTRIBUTEMETADATASQL, [attributeCode, entityTypeCode]) attributeMetadata = self.adaptor.mySQLCursor.fetchone() if attributeMetadata is None or len(attributeMetadata) < 4 : log = "Entity Type/Attribute Code: {0}/{1} does not exist".format(entityTypeCode, attributeCode) raise Exception(log) if attributeCode == 'url_key' and self.setting['VERSION'] == "EE": dataType = attributeCode else: dataType = attributeMetadata['backend_type'] return (dataType, attributeMetadata) def isEntityExit(self, entityTypeCode, entityId): sql = self.ISENTITYEXITSQL.format(entityTypeCode) self.adaptor.mySQLCursor.execute(sql, [entityId]) exist = self.adaptor.mySQLCursor.fetchone() return exist['count'] def isAttributeValueExit(self, entityTypeCode, dataType, attributeId, storeId, entityId): key = 'row_id' if self.setting['VERSION'] == "EE" else 'entity_id' sql = self.ISATTRIBUTEVALUEEXITSQL.format(entityTypeCode, dataType, key) self.adaptor.mySQLCursor.execute(sql, [attributeId, storeId, entityId]) exist = self.adaptor.mySQLCursor.fetchone() return exist['count'] def replaceAttributeValue(self, entityTypeCode, dataType, entityId, attributeId, value, storeId=0): if entityTypeCode == 'catalog_product' or entityTypeCode == 'catalog_category': cols = "entity_id, attribute_id, store_id, value" if self.setting['VERSION'] == "EE": cols = "row_id, attribute_id, store_id, value" vls = "%s, %s, {0}, %s".format(storeId) param = [entityId, attributeId, value] else: cols = "entity_id, attribute_id, value" vls = "%s, %s, %s" param = [entityId, attributeId, value] sql = self.REPLACEATTRIBUTEVALUESQL.format(entityTypeCode, dataType, cols, vls) self.adaptor.mySQLCursor.execute("SET FOREIGN_KEY_CHECKS = 0") self.adaptor.mySQLCursor.execute(sql, param) self.adaptor.mySQLCursor.execute("SET FOREIGN_KEY_CHECKS = 1") def updateEntityUpdatedAt(self, entityTypeCode, entityId): sql = self.UPDATEENTITYUPDATEDATSQL.format(entityTypeCode) self.adaptor.mySQLCursor.execute(sql, [entityId]) def setAttributeOptionValues(self, attributeId, options, entityTypeCode="catalog_product", adminStoreId=0, updateExistingOption=False): optionId = self.getOptionId(attributeId, options[adminStoreId], adminStoreId) if optionId is None: self.adaptor.mySQLCursor.execute(self.INSERTEAVATTRIBUTEOPTIONSQL, [attributeId]) optionId = self.adaptor.mySQLCursor.lastrowid for (storeId, optionValue) in options.items(): self.adaptor.mySQLCursor.execute(self.OPTIONVALUEEXISTSQL, [optionId, storeId]) exist = self.adaptor.mySQLCursor.fetchone() if not exist or exist['cnt'] == 0 : self.adaptor.mySQLCursor.execute(self.INSERTOPTIONVALUESQL, [optionId, storeId, optionValue]) elif exist['cnt'] >0 and updateExistingOption == True: self.adaptor.mySQLCursor.execute(self.UPDATEOPTIONVALUESQL, [optionValue, optionId, storeId]) return optionId def setMultiSelectOptionIds(self, attributeId, values, entityTypeCode="catalog_product", adminStoreId=0, delimiter="|"): values = values.strip('"').strip("'").strip("\n").strip() listValues = [v.strip() for v in values.split(delimiter)] listOptionIds = [] for value in listValues: options = {0: value} optionId = self.setAttributeOptionValues(attributeId, options, entityTypeCode=entityTypeCode, adminStoreId=adminStoreId) listOptionIds.append(str(optionId)) optionIds = ",".join(listOptionIds) if len(listOptionIds) > 0 else None return optionIds def getOptionId(self, attributeId, value, adminStoreId=0): self.adaptor.mySQLCursor.execute(self.GETOPTIONIDSQL, [attributeId, value, adminStoreId]) res = self.adaptor.mySQLCursor.fetchone() optionId = None if res is not None: optionId = res['option_id'] return optionId def getMultiSelectOptionIds(self, attributeId, values, adminStoreId=0, delimiter="|"): if values is None: return [None] values = values.strip('"').strip("'").strip("\n").strip() listValues = [v.strip() for v in values.split(delimiter)] listOptionIds = [] for value in listValues: optionId = self.getOptionId(attributeId, value, adminStoreId=adminStoreId) listOptionIds.append(str(optionId)) optionIds = ",".join(listOptionIds) if len(listOptionIds) > 0 else None return optionIds def getProductIdBySku(self, sku): self.adaptor.mySQLCursor.execute(self.GETPRODUCTIDBYSKUSQL, [sku]) entity = self.adaptor.mySQLCursor.fetchone() if entity is not None: entityId = int(entity["entity_id"]) else: entityId = 0 return entityId def insertCatalogProductEntity(self, sku, attributeSet='Default', typeId='simple'): entityMetadata = self.getEntityMetaData('catalog_product', attributeSet) if entityMetadata == None: return 0 if self.setting['VERSION'] == 'EE': self.adaptor.mySQLCursor.execute("""SET FOREIGN_KEY_CHECKS = 0;""") self.adaptor.mySQLCursor.execute(self.INSERTCATALOGPRODUCTENTITYEESQL, (entityMetadata['attribute_set_id'], typeId, sku)) productId = self.adaptor.mySQLCursor.lastrowid self.adaptor.mySQLCursor.execute("""UPDATE catalog_product_entity SET entity_id = row_id WHERE row_id = %s;""", (productId,)) self.adaptor.mySQLCursor.execute("""INSERT INTO sequence_product (sequence_value) VALUES (%s);""", (productId,)) self.adaptor.mySQLCursor.execute("""SET FOREIGN_KEY_CHECKS = 1;""") else: self.adaptor.mySQLCursor.execute(self.INSERTCATALOGPRODUCTENTITYSQL, (entityMetadata['attribute_set_id'], typeId, sku)) productId = self.adaptor.mySQLCursor.lastrowid return productId def updateCatalogProductEntity(self, productId, attributeSet='Default', typeId='simple'): entityMetadata = self.getEntityMetaData('catalog_product', attributeSet) if entityMetadata == None: return 0 key = 'row_id' if self.setting['VERSION'] == "EE" else 'entity_id' sql = self.UPDATECATALOGPRODUCTSQL.format(key) self.adaptor.mySQLCursor.execute(sql, [entityMetadata['attribute_set_id'], typeId, productId]) def syncProductData(self, attributeSet, entityId, data, storeId=0, adminStoreId=0): doNotUpdateOptionAttributes = ['status','visibility','tax_class_id'] for attributeCode, value in data.items(): (dataType, attributeMetadata) = self.getAttributeMetadata(attributeCode, 'catalog_product') if attributeMetadata['frontend_input'] == 'select' and attributeCode not in doNotUpdateOptionAttributes: optionId = self.getOptionId(attributeMetadata['attribute_id'], value, adminStoreId=adminStoreId) if optionId is None: options = {0: value} optionId = self.setAttributeOptionValues(attributeMetadata['attribute_id'], options, adminStoreId=adminStoreId) value = optionId elif attributeMetadata['frontend_input'] == 'multiselect': optionIds = self.getMultiSelectOptionIds(attributeMetadata['attribute_id'], value, adminStoreId=adminStoreId) if optionIds is None: optionIds = self.setMultiSelectOptionIds(attributeMetadata['attribute_id'], value, adminStoreId=adminStoreId) value = optionIds # ignore the static datatype. if dataType != "static": exist = self.isAttributeValueExit('catalog_product', dataType, attributeMetadata['attribute_id'], adminStoreId, entityId) storeId = adminStoreId if exist == 0 else storeId self.replaceAttributeValue("catalog_product", dataType, entityId, attributeMetadata['attribute_id'], value, storeId=storeId) def syncProduct(self, sku, attributeSet, data, typeId, storeId): try: productId = self.getProductIdBySku(sku) if productId == 0: productId = self.insertCatalogProductEntity(sku, attributeSet, typeId) else: self.updateCatalogProductEntity(productId, attributeSet, typeId) self.syncProductData(attributeSet, productId, data, storeId=storeId) self.adaptor.commit() return productId except Exception as e: self.adaptor.rollback() raise ## TO DO: Implement the logic def _getOrderCustomerEmail(self,order): #return self.setting['MAGE1ORDERCUSTOMEREMAIL'] return order['addresses']["billto"]['email'] ## TO DO: Implement the logic def _getOrderShippingMethod(self,order): return self.setting['MAGE1ORDERSHIPPINGMETHOD'] ## TO DO: Implement the logic def _getOrderPaymentMethod(self,order): return self.setting['MAGE1ORDERPAYMENTMETHOD'] def insertOrder(self, order): orderNum = None cartId = self.apiAdaptor.apiConn.service.shoppingCartCreate(self.apiAdaptor.apiSessionId,self.setting['MAGE1ORDERSTOREID']) self.logger.info("Shopping cart created successfully, cartId: {0}".format(cartId)) # get line items for item in order['items']: productFilter = { 'complex_filter': { 'item': { 'key': 'sku', 'value': { 'key': 'in', 'value': item['sku'] } } } } productList = self.apiAdaptor.apiConn.service.catalogProductList(self.apiAdaptor.apiSessionId, productFilter) product = productList[0] cartProduct = self.apiAdaptor.apiConn.factory.create('shoppingCartProductEntity') cartProduct['qty'] = item['qty'] cartProduct['product_id'] = product['product_id'] cartProduct['sku'] = product['sku'] # cartProduct['options'] # cartProduct['bundle_option'] # cartProduct['bundle_option_qty'] # cartProduct['links'] self.apiAdaptor.apiConn.service.shoppingCartProductAdd(self.apiAdaptor.apiSessionId, cartId, [cartProduct]) # print "cartProduct = {}".format(cartProduct) # Customer Info customerFilter = { 'complex_filter': { 'item': { 'key': 'email', 'value': { 'key': 'in', 'value': self._getOrderCustomerEmail(order) } } } } customerList = self.apiAdaptor.apiConn.service.customerCustomerList(self.apiAdaptor.apiSessionId, customerFilter) customer = customerList[0] cartCustomer = self.apiAdaptor.apiConn.factory.create('shoppingCartCustomerEntity') cartCustomer['mode'] = 'customer' cartCustomer['customer_id'] = customer['customer_id'] cartCustomer['email'] = customer['email'] cartCustomer['firstname'] = customer['firstname'] cartCustomer['lastname'] = customer['lastname'] cartCustomer['website_id'] = customer['website_id'] cartCustomer['store_id'] = customer['store_id'] cartCustomer['group_id'] = customer['group_id'] # cartCustomer['password'] # cartCustomer['confirmation'] self.apiAdaptor.apiConn.service.shoppingCartCustomerSet(self.apiAdaptor.apiSessionId, cartId, cartCustomer) # print "cartCustomer = {}".format(cartCustomer) # Bill-To Address billingAddress = self.apiAdaptor.apiConn.factory.create('shoppingCartCustomerAddressEntity') billingAddress['mode'] = 'billing' billingAddress['firstname'] = order['addresses']["billto"]['firstname'] billingAddress['lastname'] = order['addresses']["billto"]['lastname'] billingAddress['street'] = order['addresses']["billto"]['address'] if order['addresses']["shipto"]['address'].strip() != "" else "Street" billingAddress['city'] = order['addresses']["billto"]['city'] billingAddress['region'] = order['addresses']["billto"]['region'] # billingAddress['region_id'] = 12 billingAddress['postcode'] = order['addresses']["billto"]['postcode'] billingAddress['country_id'] = order['addresses']["billto"]['country'] billingAddress['telephone'] = order['addresses']["billto"]['telephone'] # billingAddress['address_id'] # billingAddress['company'] # billingAddress['fax'] # billingAddress['is_default_billing'] # billingAddress['is_default_shipping'] # print "billingAddress = {}".format(billingAddress) # Ship-To Address shippingAddress = self.apiAdaptor.apiConn.factory.create('shoppingCartCustomerAddressEntity') shippingAddress['mode'] = 'shipping' shippingAddress['firstname'] = order['addresses']["shipto"]['firstname'] shippingAddress['lastname'] = order['addresses']["shipto"]['lastname'] shippingAddress['street'] = order['addresses']["shipto"]['address'] if order['addresses']["shipto"]['address'].strip() != "" else "Street" shippingAddress['city'] = order['addresses']["shipto"]['city'] shippingAddress['region'] = order['addresses']["shipto"]['region'] # shippingAddress['region_id'] = 12 shippingAddress['postcode'] = order['addresses']["shipto"]['postcode'] shippingAddress['country_id'] = order['addresses']["shipto"]['country'] shippingAddress['telephone'] = order['addresses']["shipto"]['telephone'] self.apiAdaptor.apiConn.service.shoppingCartCustomerAddresses(self.apiAdaptor.apiSessionId, cartId, [billingAddress, shippingAddress]) # print "shippingAddress = {}".format(shippingAddress) # Shipping Method # TODO # shippingMethodCode = order['shipment_method'] shippingMethodCode = self._getOrderShippingMethod(order) try: self.apiAdaptor.apiConn.service.shoppingCartShippingMethod(self.apiAdaptor.apiSessionId, cartId, shippingMethodCode) except Exception as e: self.logger.info("Failed to set shipping method '{0}' for order {1} error: {2}".format(shippingMethodCode, order['id'], e)) # raise # TODO shippingMethodList = self.apiAdaptor.apiConn.service.shoppingCartShippingList(self.apiAdaptor.apiSessionId, cartId) # print "shippingMethodList = {}".format(shippingMethodList) shippingMethodCode = shippingMethodList[0]['code'] self.apiAdaptor.apiConn.service.shoppingCartShippingMethod(self.apiAdaptor.apiSessionId, cartId, shippingMethodCode) # Payment Method # TODO # paymentMethodCode = order['payment_method'] paymentMethodCode = self._getOrderPaymentMethod(order) paymentMethod = self.apiAdaptor.apiConn.factory.create('shoppingCartPaymentMethodEntity') paymentMethod['method'] = paymentMethodCode # paymentMethod['po_number'] # paymentMethod['cc_cid'] # paymentMethod['cc_owner'] # paymentMethod['cc_number'] # paymentMethod['cc_type'] # paymentMethod['cc_exp_year'] # paymentMethod['cc_exp_month'] try: self.apiAdaptor.apiConn.service.shoppingCartPaymentMethod(self.apiAdaptor.apiSessionId, cartId, paymentMethod) except Exception as e: self.logger.info("Failed to set payment method {0} for order {1} error: {2}".format(paymentMethodCode, order['id'], e)) # raise # TODO paymentMethodList = self.apiAdaptor.apiConn.service.shoppingCartPaymentList(self.apiAdaptor.apiSessionId, cartId) # print "paymentMethodList = {}".format(paymentMethodList) paymentMethod['method'] = paymentMethodList[0]['code'] self.apiAdaptor.apiConn.service.shoppingCartPaymentMethod(self.apiAdaptor.apiSessionId, cartId, paymentMethod) orderNum = self.apiAdaptor.apiConn.service.shoppingCartOrder(self.apiAdaptor.apiSessionId, cartId) self.logger.info("Order created successfully, order Number: {0}".format(orderNum)) return orderNum def insertOrders(self, orders): for order in orders: try: order["bo_order_id"] = self.insertOrder(order) order["tx_status"] = 'S' except Exception as e: log = traceback.format_exc() order["bo_order_id"] = "####" order["tx_status"] = 'F' order["tx_note"] = log self.logger.exception('Failed to create order: {0} with error: {1}'.format(order, e)) return orders def getAttributesByEntityTypeAndAttributeSet(self,entityTypeCode,attributeSetName): self.adaptor.mySQLCursor.execute(self.GETATTRIBUTESBYENTITYTYPEANDATTRIBUTESETSQL,[entityTypeCode, attributeSetName]) res = self.adaptor.mySQLCursor.fetchall() attributes = {} for row in res: attributes[row['attribute_code']] = row return attributes def getAttributesByEntityType(self,entityTypeCode): self.adaptor.mySQLCursor.execute(self.GETATTRIBUTESBYENTITYTYPESQL,[entityTypeCode]) res = self.adaptor.mySQLCursor.fetchall() attributes = {} for row in res: attributes[row['attribute_code']] = row return attributes def getAttributeOptionValue(self,attributeId, entityId, storeId=0): self.adaptor.mySQLCursor.execute(self.GETATTRIBUTEOPTIONVALUESQL,[attributeId, entityId, storeId]) item = self.adaptor.mySQLCursor.fetchone() if item is not None: value = item['value'] return value return None def getAttributeValue(self,entityTypeCode,attributeCode,entityId,storeId=0): attributeMetadata = self.getAttributeMetadata(attributeCode,entityTypeCode) (dataType, attributeMetadata) = self.getAttributeMetadata(attributeCode, entityTypeCode) if attributeMetadata is None: self.logger.info("Attribute metadata is not found for {0}".format(attributeCode)) return None attributeId = attributeMetadata['attribute_id'] if entityId and attributeMetadata['backend_type'] in ['varchar','text','int','decimal','datetime'] and entityId != '': if entityTypeCode in ['catalog_category','catalog_product'] : getAttributeValueSQL = self.GETATTRIBUTEVALUESQL + " AND store_id = {0}".format(storeId) else : getAttributeValueSQL = self.GETATTRIBUTEVALUESQL getAttributeValueSQL = getAttributeValueSQL.format(entityTypeCode, attributeMetadata['backend_type']) self.adaptor.mySQLCursor.execute(getAttributeValueSQL,[attributeId,entityId]) res = self.adaptor.mySQLCursor.fetchone() value = None if res is not None: value = res['value'] if value is not None and attributeMetadata['frontend_input'] == 'select' and attributeMetadata['backend_type'] == 'int': optionValue = self.getAttributeOptionValue(attributeId, value) if optionValue is not None : value = optionValue if value is not None and attributeMetadata['frontend_input'] == 'multiselect' : valueIdsList = [v.strip() for v in value.split(',')] values = [] for v in valueIdsList : vStr = self.getAttributeOptionValue(attributeId, v) if vStr is not None: values.append(vStr) value = ",".join(values) return value else : return None def getProducts(self,attributeSetName,cutDt,exportAttributes=[],limit=None,offset=None): if attributeSetName != '': attributes = self.getAttributesByEntityTypeAndAttributeSet('catalog_product',attributeSetName) else : attributes = self.getAttributesByEntityType('catalog_product') attributeSetName = "%" sql = self.EXPORTPRODUCTSSQL if offset is not None and limit is not None: sql = sql + " LIMIT {0} OFFSET {1}".format(limit,offset) elif limit is not None: sql = sql + " LIMIT {0}".format(limit) self.adaptor.mySQLCursor.execute(sql,[cutDt,attributeSetName]) products = self.adaptor.mySQLCursor.fetchall() allProducts = [] cnt = 1 for product in products : for attributeCode in attributes : if len(exportAttributes) > 0 and attributeCode not in exportAttributes: continue if attributeCode in product : if product[attributeCode] is not None : product[attributeCode] = str(product[attributeCode]) continue attributeValue = self.getAttributeValue('catalog_product',attributeCode,product['entity_id']) if attributeValue is not None: attributeValue = str(attributeValue) product[attributeCode] = attributeValue for k, v in product.items(): product[k] = str(v) allProducts.append(product) cnt = cnt + 1 return allProducts def getInventory(self, cutdt,offset=None,limit=None): sql = self.EXPORTSTOCKSQL if offset is not None and limit is not None: sql = sql + " LIMIT {0} OFFSET {1}".format(limit,offset) elif limit is not None: sql = sql + " LIMIT {0}".format(limit) self.adaptor.mySQLCursor.execute(sql,[cutdt]) res = self.adaptor.mySQLCursor.fetchall() return res def getTotalProductsCount(self,cutdt,attributeSetName='%'): self.adaptor.mySQLCursor.execute(self.EXPORTPRODUCTSCOUNTSQL,[cutdt,attributeSetName]) res = self.adaptor.mySQLCursor.fetchone() return res['total'] def getTotalInventoryCount(self,cutdt): self.adaptor.mySQLCursor.execute(self.EXPORTSTOCKCOUNTSQL,[cutdt]) res = self.adaptor.mySQLCursor.fetchone() return res['total'] def isCanInvoice(self,mOrderIncId): self.adaptor.mySQLCursor.execute(self.ISCANINVOICESQL,[mOrderIncId]) res = self.adaptor.mySQLCursor.fetchone() flag = False if res is not None: baseTotalInvoiced = res['base_total_invoiced'] baseGrandTotal = res['base_grand_total'] if baseTotalInvoiced < baseGrandTotal: flag = True return flag def getInvoiceIncIdByOrderIncId(self, orderIncId): self.adaptor.mySQLCursor.execute(self.GETINVOICEINCIDSQL, [orderIncId]) res = self.adaptor.mySQLCursor.fetchall() if len(res) > 0: invoiceIncIds = [entity['increment_id'] for entity in res] return ",".join(invoiceIncIds) else: return None def getOrderLineItemBySku(self,mOrderIncId,sku): self.adaptor.mySQLCursor.execute(self.GETORDERLINEITEMBYSKUSQL,[sku,mOrderIncId]) row = self.adaptor.mySQLCursor.fetchone() if row is not None: return int(row['item_id']) else : return None def insertInvoice(self, invoice): orderIncId = invoice['fe_order_id'] if self.isCanInvoice(orderIncId): itemsQty = {} for line in invoice['data']['items']: sku = line['sku'] qty = line['qty'] orderItemId = self.getOrderLineItemBySku(orderIncId,sku) if orderItemId is None: continue if orderItemId in itemsQty: itemsQty[orderItemId] = itemsQty[orderItemId] + qty else: itemsQty[orderItemId] = qty items = [] for k, v in itemsQty.items(): items.append({'order_item_id':str(k),'qty':str(float(v))}) params = self.apiAdaptor.apiConn.factory.create('{http://schemas.xmlsoap.org/soap/encoding/}Array') params = {'item':items} comment = '' invoiceNumber = self.apiAdaptor.apiConn.service.salesOrderInvoiceCreate( self.apiAdaptor.apiSessionId, orderIncId, params, comment, self.setting['MAGE1EMAILINVOICE'], self.setting['MAGE1INVOICEEMAILCOMMENT'] ) self.logger.info("Invoice created successfully, invoice number: {0}".format(invoiceNumber)) return invoiceNumber else : invoiceNumber = self.getInvoiceIncIdByOrderIncId(orderIncId) if invoiceNumber is not None: return invoiceNumber else: log = "Order is not able to invoiced, order number: {0}".format(orderIncId) self.logger.exception(log) raise Exception(log) def getImages(self, cutdt, offset=None, limit=None): sql = self.EXPORTMEDIAIMAGESSQL.format(self.setting["MEDIABASEURL"]) if offset is not None and limit is not None: sql = sql + " LIMIT {0} OFFSET {1}".format(limit,offset) elif limit is not None: sql = sql + " LIMIT {0}".format(limit) result = [] imageTypes = ["image", "small_image", "thumbnail"] for imageType in imageTypes: self.adaptor.mySQLCursor.execute(sql, [imageType, imageType, cutdt]) res = self.adaptor.mySQLCursor.fetchall() result.extend(res) return result def getGallery(self, cutdt, offset=None, limit=None): sql = self.EXPORTMEDIAGALLERYSQL.format(self.setting["MEDIABASEURL"]) if offset is not None and limit is not None: sql = sql + " LIMIT {0} OFFSET {1}".format(limit,offset) elif limit is not None: sql = sql + " LIMIT {0}".format(limit) self.adaptor.mySQLCursor.execute(sql, [cutdt]) res = self.adaptor.mySQLCursor.fetchall() return res
from typing import List, Optional import numpy as np from django.db.models.aggregates import Sum from scipy import special from django.core.exceptions import ObjectDoesNotExist from django.db import IntegrityError, models from django_pandas.managers import DataFrameManager from app.support.repetition import calculate_repetition, get_words from contract_models.song import SongModel from contract_models.youtube import YouTubeVideoModel from services.genius import remove_sections, tokenize_words __all__ = ['Genre', 'Song', 'YouTubeVideo', 'BillboardYearEndEntry', 'SpotifyTrack', 'SpotifySongWeeklyStream'] from services.wikidata import retrieve_songmodel_wikidata class Genre(models.Model): name = models.CharField(max_length=289) pretty_name = models.CharField(max_length=289) def as_dict(self) -> dict: return {'name': self.name} class Song(models.Model): title = models.CharField(max_length=289) lyrics = models.TextField() compressibility = models.FloatField() artists = models.ManyToManyField('Artist', through='ArtistInSong') genres = models.ManyToManyField(Genre) spotify_popularity = models.IntegerField() wikidata_id = models.CharField(max_length=13) genius_id = models.CharField(max_length=15) # image_url = models.URLField() objects = DataFrameManager() def retrieve_wikidata_id(self): """ Retrieves wikidata from title, artist genre? """ if self.wikidata_id: return aw_ids = [a.wikidata_id for a in self.artists] song: SongModel = retrieve_songmodel_wikidata(song_title=self.title, artists=aw_ids) self.wikidata_id = song.wikidata_id self.save() @property def artist_names(self) -> str: return ",".join([a.title for a in self.artists.all()]) @property def words(self) -> List[str]: return list(get_words(remove_sections(self.lyrics))) @property def word_count(self) -> int: return len(tokenize_words(self.lyrics)) @property def identifier(self) -> str: return f"{self.title} - {self.artist_names}" @property def weighted_popularity(self) -> Optional[float]: """ Returns a number between 0 and 1 """ yt_vid = self.youtubevideo_set.all() if len(yt_vid) == 0: raise ValueError("No yt") popularity = list(yt_vid)[0].view_count if popularity: w = float(special.expit((np.log10(popularity) - 5.2) / 0.3)) assert 0 <= w <= 1, f"weighted_popularity of {self} not in bound: {w}" return w else: raise ValueError("The view count does not exist.") def update_compression_ratio(self): new = calculate_repetition(remove_sections(self.lyrics)) print(f"Updating model {self}\n" f"old: {self.compressibility}\n" f"new: {new}") self.compressibility = new self.save() class YouTubeVideo(models.Model): song = models.ForeignKey(Song, on_delete=models.CASCADE) # song = models.OneToOneField(Song, on_delete=models.CASCADE) video_id = models.CharField(max_length=255, unique=True, blank=False) title = models.CharField(max_length=255, null=True) view_count = models.BigIntegerField(null=True) like_count = models.BigIntegerField(null=True) dislike_count = models.BigIntegerField(null=True) favorite_count = models.BigIntegerField(null=True) comment_count = models.BigIntegerField(null=True) default_language = models.CharField(max_length=10, null=True) published_at = models.DateTimeField(null=True) channel_title = models.CharField(max_length=255, null=True) channel_id = models.CharField(max_length=255, null=True) @classmethod def update_all_video_stats(cls, start = 0): all_vid_count = cls.objects.all().count() current = start while current < all_vid_count: end = current + YouTubeVideoModel.HARD_LIMIT models = YouTubeVideoModel.from_video_ids(list(dct['video_id'] for dct in cls.objects.all() [current: end].values('video_id'))) for m in models: inst = cls.objects.get(video_id=m.video_id) inst.update_stats(m) current += YouTubeVideoModel.HARD_LIMIT @classmethod def upsert_video(cls, video_id: str, song: Song, **kwargs) -> 'YouTubeVideo': try: video = YouTubeVideo.objects.get(video_id=video_id) except ObjectDoesNotExist: video = YouTubeVideo(video_id=video_id, song=song, **kwargs) try: video.save() except IntegrityError as e: print(f'Fail {video.title} {song.title} - {e}') # video.update_stats() return video def update_stats(self, yt_video = None): if yt_video is None: yt_video = YouTubeVideoModel.from_video_id(self.video_id) if not self.title: self.title = yt_video.title self.view_count = yt_video.view_count self.like_count = yt_video.like_count self.favorite_count = yt_video.favorite_count self.dislike_count = yt_video.dislike_count self.comment_count = yt_video.comment_count self.published_at = yt_video.published_at self.channel_title = yt_video.channel_title self.channel_id = yt_video.channel_id self.default_language = yt_video.default_language self.save() class BillboardYearEndEntry(models.Model): chart = models.CharField(max_length=255) image_url = models.CharField(max_length=255) artist = models.CharField(max_length=255) title = models.CharField(max_length=255) year = models.IntegerField() rank = models.IntegerField() song = models.OneToOneField(Song, on_delete=models.CASCADE) @classmethod def from_dict(cls, dct: dict, song: Song) -> 'BillboardYearEndEntry': try: entry = cls.objects.get(song=song) return entry except cls.DoesNotExist: chart = dct['chart'] year = dct['year'] rank = dct['rank'] title = dct['title'] artist = dct['artist'] image_url = dct['image'] entry = cls(chart=chart, year=year, rank=rank, title=title, artist=artist, image_url=image_url, song=song) return entry class SpotifyTrack(models.Model): song = models.OneToOneField(Song, on_delete=models.CASCADE) track_id = models.CharField(max_length=255, unique=True) album_id = models.CharField(max_length=255) @classmethod def upsert(cls, track_id: str, song: Song, **kwargs) -> 'YouTubeVideo': try: video = cls.objects.get(track_id=track_id) except ObjectDoesNotExist: video = cls(track_id=track_id, song=song, **kwargs) try: video.save() except IntegrityError as e: print(f'Fail {video.track_id} {song.title} - {e}') return video class SpotifySongWeeklyStream(models.Model): song = models.OneToOneField(Song, on_delete=models.CASCADE) streams = models.BigIntegerField() week_date = models.CharField(max_length=255)
import re import bempp.api import os import numpy as np import time import pbj.mesh.mesh_tools as mesh_tools import pbj.mesh.charge_tools as charge_tools import pbj.electrostatics.pb_formulation.formulations as pb_formulations import pbj.electrostatics.utils as utils class Solute: """The basic Solute object This object holds all the solute information and allows for an easy way to hold the data """ def __init__( self, solute_file_path, external_mesh_file=None, save_mesh_build_files=False, mesh_build_files_dir="mesh_files/", mesh_density=1.0, nanoshaper_grid_scale=None, mesh_probe_radius=1.4, mesh_generator="nanoshaper", print_times=False, force_field="amber", formulation="direct", ): if not os.path.isfile(solute_file_path): print("file does not exist -> Cannot start") return self._pb_formulation = formulation self.formulation_object = getattr(pb_formulations, self.pb_formulation, None) if self.formulation_object is None: raise ValueError("Unrecognised formulation type %s" % self.pb_formulation) self.force_field = force_field self.save_mesh_build_files = save_mesh_build_files self.mesh_build_files_dir = os.path.abspath(mesh_build_files_dir) if nanoshaper_grid_scale is not None: if mesh_generator == "nanoshaper": print("Using specified grid_scale.") self.nanoshaper_grid_scale = nanoshaper_grid_scale else: print( "Ignoring specified grid scale as mesh_generator is not specified as nanoshaper." ) self.mesh_density = mesh_density else: self.mesh_density = mesh_density if mesh_generator == "nanoshaper": self.nanoshaper_grid_scale = ( mesh_tools.density_to_nanoshaper_grid_scale_conversion( self.mesh_density ) ) self.mesh_probe_radius = mesh_probe_radius self.mesh_generator = mesh_generator self.print_times = print_times file_extension = solute_file_path.split(".")[-1] if file_extension == "pdb": self.imported_file_type = "pdb" self.pdb_path = solute_file_path self.solute_name = get_name_from_pdb(self.pdb_path) elif file_extension == "pqr": self.imported_file_type = "pqr" self.pqr_path = solute_file_path self.solute_name = os.path.split(solute_file_path.split(".")[-2])[-1] else: print("File is not pdb or pqr -> Cannot start") if external_mesh_file is not None: filename, file_extension = os.path.splitext(external_mesh_file) if file_extension == "": # Assume use of vert and face self.external_mesh_face_path = external_mesh_file + ".face" self.external_mesh_vert_path = external_mesh_file + ".vert" self.mesh = mesh_tools.import_msms_mesh( self.external_mesh_face_path, self.external_mesh_vert_path ) else: # Assume use of file that can be directly imported into bempp self.external_mesh_file_path = external_mesh_file self.mesh = bempp.api.import_grid(self.external_mesh_file_path) self.q, self.x_q, self.r_q = charge_tools.load_charges_to_solute( self ) # Import charges from given file else: # Generate mesh from given pdb or pqr, and import charges at the same time ( self.mesh, self.q, self.x_q, self.r_q ) = charge_tools.generate_msms_mesh_import_charges(self) self.ep_in = 4.0 self.ep_ex = 80.0 self.kappa = 0.125 self.pb_formulation_alpha = 1.0 # np.nan self.pb_formulation_beta = self.ep_ex / self.ep_in # np.nan self.pb_formulation_stern_width = 2.0 self.stern_object = None self.slic_alpha = 0.5 self.slic_beta = -60 self.slic_gamma = -0.5 self.slic_max_iterations = 20 self.slic_tolerance = 1e-5 self.pb_formulation_preconditioning = False self.pb_formulation_preconditioning_type = "calderon_squared" self.discrete_form_type = "strong" self.gmres_tolerance = 1e-5 self.gmres_restart = 1000 self.gmres_max_iterations = 1000 self.operator_assembler = "dense" self.rhs_constructor = "numpy" self.matrices = dict() self.rhs = dict() self.results = dict() self.timings = dict() # Setup Dirichlet and Neumann spaces to use, save these as object vars dirichl_space = bempp.api.function_space(self.mesh, "P", 1) # neumann_space = bempp.api.function_space(self.mesh, "P", 1) neumann_space = dirichl_space self.dirichl_space = dirichl_space self.neumann_space = neumann_space @property def pb_formulation(self): return self._pb_formulation @pb_formulation.setter def pb_formulation(self, value): self._pb_formulation = value self.formulation_object = getattr(pb_formulations, self.pb_formulation, None) self.matrices["preconditioning_matrix_gmres"] = None if self.formulation_object is None: raise ValueError("Unrecognised formulation type %s" % self.pb_formulation) @property def stern_mesh_density(self): return self._stern_mesh_density @stern_mesh_density.setter def stern_mesh_density(self, value): self._stern_mesh_density = value pb_formulations.direct_stern.create_stern_mesh(self) def display_available_formulations(self): from inspect import getmembers, ismodule print("Current formulation: " + self.pb_formulation) print("List of available formulations:") available = getmembers(pb_formulations, ismodule) for element in available: if element[0] == 'common': available.remove(element) for name, object_address in available: print(name) def display_available_preconditioners(self): from inspect import getmembers, isfunction print( "List of preconditioners available for the current formulation (" + self.pb_formulation + "):" ) for name, object_address in getmembers(self.formulation_object, isfunction): if name.endswith("preconditioner"): name_removed = name[:-15] print(name_removed) def initialise_matrices(self): start_time = time.time() # Start the timing for the matrix construction # Construct matrices based on the desired formulation # Verify if parameters are already set and save A matrix if self.formulation_object.verify_parameters(self): self.formulation_object.lhs(self) self.timings["time_matrix_initialisation"] = time.time() - start_time def assemble_matrices(self): start_assembly = time.time() self.matrices["A"].weak_form() self.timings["time_matrix_assembly"] = time.time() - start_assembly def initialise_rhs(self): start_rhs = time.time() # Verify if parameters are already set and then save RHS if self.formulation_object.verify_parameters(self): self.formulation_object.rhs(self) self.timings["time_rhs_initialisation"] = time.time() - start_rhs def apply_preconditioning(self): preconditioning_start_time = time.time() if self.pb_formulation_preconditioning: precon_str = self.pb_formulation_preconditioning_type + "_preconditioner" preconditioning_object = getattr(self.formulation_object, precon_str, None) if preconditioning_object is not None: preconditioning_object(self) else: raise ValueError( "Unrecognised preconditioning type %s for current formulation type %s" % (self.pb_formulation_preconditioning_type, self.pb_formulation) ) else: self.matrices["A_final"] = self.matrices["A"] self.rhs["rhs_final"] = [rhs for key,rhs in sorted(self.rhs.items())][:len(self.matrices["A"].domain_spaces)] self.matrices["A_discrete"] = matrix_to_discrete_form( self.matrices["A_final"], "weak" ) self.rhs["rhs_discrete"] = rhs_to_discrete_form( self.rhs["rhs_final"], "weak", self.matrices["A"] ) self.timings["time_preconditioning"] = time.time() - preconditioning_start_time def calculate_potential(self, rerun_all=False): if self.formulation_object.verify_parameters(self): self.formulation_object.calculate_potential(self, rerun_all) def calculate_solvation_energy(self, rerun_all=False): if rerun_all: self.calculate_potential(rerun_all) if "phi" not in self.results: # If surface potential has not been calculated, calculate it now self.calculate_potential() start_time = time.time() solution_dirichl = self.results["phi"] solution_neumann = self.results["d_phi"] from bempp.api.operators.potential.laplace import single_layer, double_layer slp_q = single_layer(self.neumann_space, self.x_q.transpose()) dlp_q = double_layer(self.dirichl_space, self.x_q.transpose()) phi_q = slp_q * solution_neumann - dlp_q * solution_dirichl # total solvation energy applying constant to get units [kcal/mol] total_energy = 2 * np.pi * 332.064 * np.sum(self.q * phi_q).real self.results["solvation_energy"] = total_energy self.timings["time_calc_energy"] = time.time() - start_time if self.print_times: print( "It took ", self.timings["time_calc_energy"], " seconds to compute the solvation energy", ) def calculate_gradient_field(self, h=0.001, rerun_all=False): """ Compute the first derivate of potential due to solvent in the position of the points """ if rerun_all: self.calculate_potential(rerun_all) if "phi" not in self.results: # If surface potential has not been calculated, calculate it now self.calculate_potential() start_time = time.time() solution_dirichl = self.results["phi"] solution_neumann = self.results["d_phi"] dphidr = np.zeros([len(self.x_q), 3]) dist = np.diag([h,h,h]) #matriz 3x3 diagonal de h # x axis derivate dx = np.concatenate((self.x_q[:] + dist[0], self.x_q[:] - dist[0])) # vector x+h y luego x-h slpo = bempp.api.operators.potential.laplace.single_layer(self.neumann_space, dx.transpose()) dlpo = bempp.api.operators.potential.laplace.double_layer(self.dirichl_space, dx.transpose()) phi = slpo.evaluate(solution_neumann) - dlpo.evaluate(solution_dirichl) dphidx = 0.5*(phi[0,:len(self.x_q)] - phi[0,len(self.x_q):])/h dphidr[:,0] = dphidx #y axis derivate dy = np.concatenate((self.x_q[:] + dist[1], self.x_q[:] - dist[1])) slpo = bempp.api.operators.potential.laplace.single_layer(self.neumann_space, dy.transpose()) dlpo = bempp.api.operators.potential.laplace.double_layer(self.dirichl_space, dy.transpose()) phi = slpo.evaluate(solution_neumann) - dlpo.evaluate(solution_dirichl) dphidy = 0.5*(phi[0,:len(self.x_q)] - phi[0,len(self.x_q):])/h dphidr[:,1] = dphidy #z axis derivate dz = np.concatenate((self.x_q[:] + dist[2], self.x_q[:] - dist[2])) slpo = bempp.api.operators.potential.laplace.single_layer(self.neumann_space, dz.transpose()) dlpo = bempp.api.operators.potential.laplace.double_layer(self.dirichl_space, dz.transpose()) phi = slpo.evaluate(solution_neumann) - dlpo.evaluate(solution_dirichl) dphidz = 0.5*(phi[0,:len(self.x_q)] - phi[0,len(self.x_q):])/h dphidr[:,2] = dphidz self.results["dphidr_charges"] = dphidr self.timings["time_calc_gradient_field"] = time.time() - start_time if self.print_times: print( "It took ", self.timings["time_calc_gradient_field"], " seconds to compute the gradient field on solute charges", ) return None def calculate_charges_forces(self, h=0.001, rerun_all=False): if rerun_all: self.calculate_potential(rerun_all) self.calculate_gradient_field(h=h) if "phi" not in self.results: # If surface potential has not been calculated, calculate it now self.calculate_potential() if 'dphidr_charges' not in self.results: # If gradient field has not been calculated, calculate it now self.calculate_gradient_field(h=h) start_time = time.time() dphidr = self.results["dphidr_charges"] convert_to_kcalmolA = 4 * np.pi * 332.0636817823836 f_reac = convert_to_kcalmolA * -np.transpose(np.transpose(dphidr)*self.q) f_reactotal = np.sum(f_reac,axis=0) self.results["f_qf_charges"] = f_reac self.results["f_qf"] = f_reactotal self.timings["time_calc_solute_force"] = time.time() - start_time if self.print_times: print( "It took ", self.timings["time_calc_solute_force"], " seconds to compute the force on solute charges", ) return None def calculate_boundary_forces(self, rerun_all=False): if rerun_all: self.calculate_potential(rerun_all) if "phi" not in self.results: # If surface potential has not been calculated, calculate it now self.calculate_potential() start_time = time.time() phi = self.results['phi'].evaluate_on_element_centers() d_phi = self.results['d_phi'].evaluate_on_element_centers() convert_to_kcalmolA = 4 * np.pi * 332.0636817823836 dS = np.transpose(np.transpose(self.mesh.normals)*self.mesh.volumes) #Dielectric boundary force f_db = - 0.5 * convert_to_kcalmolA * (self.ep_ex-self.ep_in) * (self.ep_in/self.ep_ex) * \ np.sum(np.transpose(np.transpose(dS)*d_phi[0]**2),axis=0) #Ionic boundary force f_ib = - 0.5 * convert_to_kcalmolA * (self.ep_ex)*(self.kappa**2) * \ np.sum(np.transpose(np.transpose(dS)*phi[0]**2),axis=0) self.results["f_db"] = f_db self.results["f_ib"] = f_ib self.timings["time_calc_boundary_force"] = time.time() - start_time if self.print_times: print( "It took ", self.timings["time_calc_boundary_force"], " seconds to compute the boundary forces", ) return None def calculate_solvation_forces(self, h=0.001, rerun_all=False): if rerun_all: self.calculate_potential(rerun_all) self.calculate_gradient_field(h=h) self.calculate_charges_forces() self.calculate_boundary_forces() if "phi" not in self.results: # If surface potential has not been calculated, calculate it now self.calculate_potential() if 'f_qf' not in self.results: self.calculate_gradient_field(h=h) self.calculate_charges_forces() if 'f_db' not in self.results: self.calculate_boundary_forces() start_time = time.time() f_solv = np.zeros([3]) f_qf = self.results["f_qf"] f_db = self.results["f_db"] f_ib = self.results["f_ib"] f_solv = f_qf + f_db + f_ib self.results['f_solv'] = f_solv self.timings["time_calc_solvation_force"] = time.time() - start_time \ + self.timings['time_calc_boundary_force'] \ + self.timings['time_calc_solute_force'] \ + self.timings["time_calc_gradient_field"] if self.print_times: print( "It took ", self.timings["time_calc_solvation_force"], " seconds to compute the solvation forces", ) return None def get_name_from_pdb(pdb_path): pdb_file = open(pdb_path) first_line = pdb_file.readline() first_line_split = re.split(r"\s{2,}", first_line) solute_name = first_line_split[3].lower() pdb_file.close() return solute_name def matrix_to_discrete_form(matrix, discrete_form_type): if discrete_form_type == "strong": matrix_discrete = matrix.strong_form() elif discrete_form_type == "weak": matrix_discrete = matrix.weak_form() else: raise ValueError("Unexpected discrete type: %s" % discrete_form_type) return matrix_discrete def rhs_to_discrete_form(rhs_list, discrete_form_type, A): from bempp.api.assembly.blocked_operator import ( coefficients_from_grid_functions_list, projections_from_grid_functions_list, ) if discrete_form_type == "strong": rhs = coefficients_from_grid_functions_list(rhs_list) elif discrete_form_type == "weak": rhs = projections_from_grid_functions_list(rhs_list, A.dual_to_range_spaces) else: raise ValueError("Unexpected discrete form: %s" % discrete_form_type) return rhs
<gh_stars>0 # -*- coding: utf-8 -*- from datetime import datetime, timedelta from openprocurement.auctions.core.utils import get_now def post_auction_auction(self): self.app.authorization = ('Basic', ('auction', '')) response = self.app.post_json('/auctions/{}/auction'.format(self.auction_id), {'data': {}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]["description"], "Can't report auction results in current (active.tendering) auction status") self.set_status('active.auction') response = self.app.post_json('/auctions/{}/auction'.format(self.auction_id), {'data': {'bids': [{'invalid_field': 'invalid_value'}]}}, status=422) self.assertEqual(response.status, '422 Unprocessable Entity') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'], [ {u'description': {u'invalid_field': u'Rogue field'}, u'location': u'body', u'name': u'bids'} ]) patch_data = { 'bids': [ { "id": self.initial_bids[1]['id'], "value": { "amount": 419, "currency": "UAH", "valueAddedTaxIncluded": True } } ] } response = self.app.post_json('/auctions/{}/auction'.format(self.auction_id), {'data': patch_data}, status=422) self.assertEqual(response.status, '422 Unprocessable Entity') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]["description"], "Number of auction results did not match the number of auction bids") patch_data['bids'].append({ "value": { "amount": 409, "currency": "UAH", "valueAddedTaxIncluded": True } }) patch_data['bids'][1]['id'] = "some_id" response = self.app.post_json('/auctions/{}/auction'.format(self.auction_id), {'data': patch_data}, status=422) self.assertEqual(response.status, '422 Unprocessable Entity') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]["description"], {u'id': [u'Hash value is wrong length.']}) patch_data['bids'][1]['id'] = "00000000000000000000000000000000" response = self.app.post_json('/auctions/{}/auction'.format(self.auction_id), {'data': patch_data}, status=422) self.assertEqual(response.status, '422 Unprocessable Entity') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]["description"], "Auction bids should be identical to the auction bids") patch_data['bids'][1]['id'] = self.initial_bids[0]['id'] response = self.app.post_json('/auctions/{}/auction'.format(self.auction_id), {'data': patch_data}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') auction = response.json['data'] self.assertNotEqual(auction["bids"][0]['value']['amount'], self.initial_bids[0]['value']['amount']) self.assertNotEqual(auction["bids"][1]['value']['amount'], self.initial_bids[1]['value']['amount']) self.assertEqual(auction["bids"][0]['value']['amount'], patch_data["bids"][1]['value']['amount']) self.assertEqual(auction["bids"][1]['value']['amount'], patch_data["bids"][0]['value']['amount']) self.assertEqual('active.qualification', auction["status"]) for i, status in enumerate(['pending.verification', 'pending.waiting']): self.assertIn("tenderers", auction["bids"][i]) self.assertIn("name", auction["bids"][i]["tenderers"][0]) # self.assertIn(auction["awards"][0]["id"], response.headers['Location']) self.assertEqual(auction["awards"][i]['bid_id'], patch_data["bids"][i]['id']) self.assertEqual(auction["awards"][i]['value']['amount'], patch_data["bids"][i]['value']['amount']) self.assertEqual(auction["awards"][i]['suppliers'], self.initial_bids[i]['tenderers']) self.assertEqual(auction["awards"][i]['status'], status) if status == 'pending.verification': self.assertIn("verificationPeriod", auction["awards"][i]) response = self.app.post_json('/auctions/{}/auction'.format(self.auction_id), {'data': patch_data}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]["description"], "Can't report auction results in current (active.qualification) auction status") def post_auction_auction_lot(self): self.app.authorization = ('Basic', ('auction', '')) response = self.app.post_json('/auctions/{}/auction'.format(self.auction_id), {'data': {}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]["description"], "Can't report auction results in current (active.tendering) auction status") self.set_status('active.auction') response = self.app.post_json('/auctions/{}/auction'.format(self.auction_id), {'data': {'bids': [{'invalid_field': 'invalid_value'}]}}, status=422) self.assertEqual(response.status, '422 Unprocessable Entity') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'], [ {u'description': {u'invalid_field': u'Rogue field'}, u'location': u'body', u'name': u'bids'} ]) patch_data = { 'bids': [ { "id": self.initial_bids[1]['id'], 'lotValues': [ { "value": { "amount": 419, "currency": "UAH", "valueAddedTaxIncluded": True } } ] } ] } response = self.app.post_json('/auctions/{}/auction'.format(self.auction_id), {'data': patch_data}, status=422) self.assertEqual(response.status, '422 Unprocessable Entity') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]["description"], "Number of auction results did not match the number of auction bids") patch_data['bids'].append({ 'lotValues': [ { "value": { "amount": 409, "currency": "UAH", "valueAddedTaxIncluded": True } } ] }) patch_data['bids'][1]['id'] = "some_id" response = self.app.post_json('/auctions/{}/auction'.format(self.auction_id), {'data': patch_data}, status=422) self.assertEqual(response.status, '422 Unprocessable Entity') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]["description"], {u'id': [u'Hash value is wrong length.']}) patch_data['bids'][1]['id'] = "00000000000000000000000000000000" response = self.app.post_json('/auctions/{}/auction'.format(self.auction_id), {'data': patch_data}, status=422) self.assertEqual(response.status, '422 Unprocessable Entity') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]["description"], "Auction bids should be identical to the auction bids") patch_data['bids'][1]['id'] = self.initial_bids[0]['id'] for lot in self.initial_lots: response = self.app.post_json('/auctions/{}/auction/{}'.format(self.auction_id, lot['id']), {'data': patch_data}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') auction = response.json['data'] self.assertNotEqual(auction["bids"][0]['lotValues'][0]['value']['amount'], self.initial_bids[0]['lotValues'][0]['value']['amount']) self.assertNotEqual(auction["bids"][1]['lotValues'][0]['value']['amount'], self.initial_bids[1]['lotValues'][0]['value']['amount']) self.assertEqual(auction["bids"][0]['lotValues'][0]['value']['amount'], patch_data["bids"][1]['lotValues'][0]['value']['amount']) self.assertEqual(auction["bids"][1]['lotValues'][0]['value']['amount'], patch_data["bids"][0]['lotValues'][0]['value']['amount']) self.assertEqual('active.qualification', auction["status"]) for i, status in enumerate(['pending.verification', 'pending.waiting']): self.assertIn("tenderers", auction["bids"][i]) self.assertIn("name", auction["bids"][i]["tenderers"][0]) # self.assertIn(auction["awards"][0]["id"], response.headers['Location']) self.assertEqual(auction["awards"][i]['bid_id'], patch_data["bids"][i]['id']) self.assertEqual(auction["awards"][i]['value']['amount'], patch_data["bids"][i]['lotValues'][0]['value']['amount']) self.assertEqual(auction["awards"][i]['suppliers'], self.initial_bids[i]['tenderers']) self.assertEqual(auction["awards"][i]['status'], status) if status == 'pending.verification': self.assertIn("verificationPeriod", auction["awards"][i]) response = self.app.post_json('/auctions/{}/auction'.format(self.auction_id), {'data': patch_data}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]["description"], "Can't report auction results in current (active.qualification) auction status") def post_auction_auction_2_lots(self): self.app.authorization = ('Basic', ('auction', '')) response = self.app.post_json('/auctions/{}/auction'.format(self.auction_id), {'data': {}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]["description"], "Can't report auction results in current (active.tendering) auction status") self.set_status('active.auction') response = self.app.post_json('/auctions/{}/auction'.format(self.auction_id), {'data': {'bids': [{'invalid_field': 'invalid_value'}]}}, status=422) self.assertEqual(response.status, '422 Unprocessable Entity') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'], [ {u'description': {u'invalid_field': u'Rogue field'}, u'location': u'body', u'name': u'bids'} ]) patch_data = { 'bids': [ { "id": self.initial_bids[1]['id'], 'lotValues': [ { "value": { "amount": 419, "currency": "UAH", "valueAddedTaxIncluded": True } } ] } ] } response = self.app.post_json('/auctions/{}/auction'.format(self.auction_id), {'data': patch_data}, status=422) self.assertEqual(response.status, '422 Unprocessable Entity') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]["description"], "Number of auction results did not match the number of auction bids") patch_data['bids'].append({ 'lotValues': [ { "value": { "amount": 409, "currency": "UAH", "valueAddedTaxIncluded": True } } ] }) patch_data['bids'][1]['id'] = "some_id" response = self.app.post_json('/auctions/{}/auction'.format(self.auction_id), {'data': patch_data}, status=422) self.assertEqual(response.status, '422 Unprocessable Entity') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]["description"], {u'id': [u'Hash value is wrong length.']}) patch_data['bids'][1]['id'] = "00000000000000000000000000000000" response = self.app.post_json('/auctions/{}/auction'.format(self.auction_id), {'data': patch_data}, status=422) self.assertEqual(response.status, '422 Unprocessable Entity') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]["description"], "Auction bids should be identical to the auction bids") patch_data['bids'][1]['id'] = self.initial_bids[0]['id'] response = self.app.post_json('/auctions/{}/auction'.format(self.auction_id), {'data': patch_data}, status=422) self.assertEqual(response.status, '422 Unprocessable Entity') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]["description"], [{"lotValues": ["Number of lots of auction results did not match the number of auction lots"]}]) for bid in patch_data['bids']: bid['lotValues'] = [bid['lotValues'][0].copy() for i in self.initial_lots] patch_data['bids'][0]['lotValues'][1]['relatedLot'] = self.initial_bids[0]['lotValues'][0]['relatedLot'] response = self.app.patch_json('/auctions/{}/auction'.format(self.auction_id), {'data': patch_data}, status=422) self.assertEqual(response.status, '422 Unprocessable Entity') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]["description"], [{u'lotValues': [{u'relatedLot': [u'relatedLot should be one of lots of bid']}]}]) patch_data['bids'][0]['lotValues'][1]['relatedLot'] = self.initial_bids[0]['lotValues'][1]['relatedLot'] for lot in self.initial_lots: response = self.app.post_json('/auctions/{}/auction/{}'.format(self.auction_id, lot['id']), {'data': patch_data}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') auction = response.json['data'] self.assertNotEqual(auction["bids"][0]['lotValues'][0]['value']['amount'], self.initial_bids[0]['lotValues'][0]['value']['amount']) self.assertNotEqual(auction["bids"][1]['lotValues'][0]['value']['amount'], self.initial_bids[1]['lotValues'][0]['value']['amount']) self.assertEqual(auction["bids"][0]['lotValues'][0]['value']['amount'], patch_data["bids"][1]['lotValues'][0]['value']['amount']) self.assertEqual(auction["bids"][1]['lotValues'][0]['value']['amount'], patch_data["bids"][0]['lotValues'][0]['value']['amount']) self.assertEqual('active.qualification', auction["status"]) for i, status in enumerate(['pending.verification', 'pending.waiting']): self.assertIn("tenderers", auction["bids"][i]) self.assertIn("name", auction["bids"][i]["tenderers"][0]) # self.assertIn(auction["awards"][0]["id"], response.headers['Location']) self.assertEqual(auction["awards"][i]['bid_id'], patch_data["bids"][i]['id']) self.assertEqual(auction["awards"][i]['value']['amount'], patch_data["bids"][i]['lotValues'][0]['value']['amount']) self.assertEqual(auction["awards"][i]['suppliers'], self.initial_bids[i]['tenderers']) self.assertEqual(auction["awards"][i]['status'], status) if status == 'pending.verification': self.assertIn("verificationPeriod", auction["awards"][i]) response = self.app.post_json('/auctions/{}/auction'.format(self.auction_id), {'data': patch_data}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]["description"], "Can't report auction results in current (active.qualification) auction status") def get_auction_auction_features(self): response = self.app.get('/auctions/{}/auction'.format(self.auction_id)) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') auction = response.json['data'] self.assertNotEqual(auction, self.initial_data) self.assertIn('dateModified', auction) self.assertIn('minimalStep', auction) self.assertNotIn("procuringEntity", auction) self.assertNotIn("tenderers", auction["bids"][0]) self.assertEqual(auction["bids"][0]['value']['amount'], self.initial_bids[0]['value']['amount']) self.assertEqual(auction["bids"][1]['value']['amount'], self.initial_bids[1]['value']['amount']) self.assertIn('features', auction) self.assertIn('parameters', auction["bids"][0]) # LeaseAuctionBridgePatchPeriod def set_auction_period(self): self.app.authorization = ('Basic', ('chronograph', '')) response = self.app.patch_json('/auctions/{}'.format(self.auction_id), {'data': {'id': self.auction_id}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['data']["status"], 'active.tendering') if self.initial_lots: item = response.json['data']["lots"][0] else: item = response.json['data'] self.assertIn('auctionPeriod', item) self.assertIn('shouldStartAfter', item['auctionPeriod']) self.assertGreaterEqual(item['auctionPeriod']['shouldStartAfter'], response.json['data']['tenderPeriod']['endDate']) self.assertIn('T00:00:00+', item['auctionPeriod']['shouldStartAfter']) self.assertEqual(response.json['data']['next_check'], response.json['data']['tenderPeriod']['endDate']) self.app.authorization = ('Basic', ('auction', '')) if self.initial_lots: response = self.app.patch_json('/auctions/{}'.format(self.auction_id), {'data': {"lots": [{"auctionPeriod": {"startDate": "9999-01-01T00:00:00+00:00"}}]}}) item = response.json['data']["lots"][0] else: response = self.app.patch_json('/auctions/{}'.format(self.auction_id), {'data': {"auctionPeriod": {"startDate": "9999-01-01T00:00:00+00:00"}}}) item = response.json['data'] self.assertEqual(response.status, '200 OK') self.assertEqual(item['auctionPeriod']['startDate'], '9999-01-01T00:00:00+00:00') if self.initial_lots: response = self.app.patch_json('/auctions/{}'.format(self.auction_id), {'data': {"lots": [{"auctionPeriod": {"startDate": None}}]}}) item = response.json['data']["lots"][0] else: response = self.app.patch_json('/auctions/{}'.format(self.auction_id), {'data': {"auctionPeriod": {"startDate": None}}}) item = response.json['data'] self.assertEqual(response.status, '200 OK') self.assertNotIn('startDate', item['auctionPeriod']) def reset_auction_period(self): self.app.authorization = ('Basic', ('chronograph', '')) response = self.app.patch_json('/auctions/{}'.format(self.auction_id), {'data': {'id': self.auction_id}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['data']["status"], 'active.tendering') if self.initial_lots: item = response.json['data']["lots"][0] else: item = response.json['data'] self.assertIn('auctionPeriod', item) self.assertIn('shouldStartAfter', item['auctionPeriod']) self.assertGreaterEqual(item['auctionPeriod']['shouldStartAfter'], response.json['data']['tenderPeriod']['endDate']) self.assertEqual(response.json['data']['next_check'], response.json['data']['tenderPeriod']['endDate']) self.app.authorization = ('Basic', ('auction', '')) if self.initial_lots: response = self.app.patch_json('/auctions/{}'.format(self.auction_id), {'data': {"lots": [{"auctionPeriod": {"startDate": "9999-01-01T00:00:00"}}]}}) item = response.json['data']["lots"][0] else: response = self.app.patch_json('/auctions/{}'.format(self.auction_id), {'data': {"auctionPeriod": {"startDate": "9999-01-01T00:00:00"}}}) item = response.json['data'] self.assertEqual(response.status, '200 OK') self.assertGreaterEqual(item['auctionPeriod']['shouldStartAfter'], response.json['data']['tenderPeriod']['endDate']) self.assertIn('9999-01-01T00:00:00', item['auctionPeriod']['startDate']) self.set_status('active.auction', {'status': 'active.tendering'}) self.app.authorization = ('Basic', ('chronograph', '')) response = self.app.patch_json('/auctions/{}'.format(self.auction_id), {'data': {'id': self.auction_id}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.json['data']["status"], 'active.auction') item = response.json['data']["lots"][0] if self.initial_lots else response.json['data'] self.assertGreaterEqual(item['auctionPeriod']['shouldStartAfter'], response.json['data']['tenderPeriod']['endDate']) self.app.authorization = ('Basic', ('auction', '')) if self.initial_lots: response = self.app.patch_json('/auctions/{}'.format(self.auction_id), {'data': {"lots": [{"auctionPeriod": {"startDate": "9999-01-01T00:00:00"}}]}}) item = response.json['data']["lots"][0] else: response = self.app.patch_json('/auctions/{}'.format(self.auction_id), {'data': {"auctionPeriod": {"startDate": "9999-01-01T00:00:00"}}}) item = response.json['data'] self.assertEqual(response.status, '200 OK') self.assertEqual(response.json['data']["status"], 'active.auction') self.assertGreaterEqual(item['auctionPeriod']['shouldStartAfter'], response.json['data']['tenderPeriod']['endDate']) self.assertIn('9999-01-01T00:00:00', item['auctionPeriod']['startDate']) self.assertIn('9999-01-01T00:00:00', response.json['data']['next_check']) now = get_now().isoformat() auction = self.db.get(self.auction_id) if self.initial_lots: auction['lots'][0]['auctionPeriod']['startDate'] = now else: auction['auctionPeriod']['startDate'] = now self.db.save(auction) response = self.app.patch_json('/auctions/{}'.format(self.auction_id), {'data': {'id': self.auction_id}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.json['data']["status"], 'active.auction') item = response.json['data']["lots"][0] if self.initial_lots else response.json['data'] self.assertGreaterEqual(item['auctionPeriod']['shouldStartAfter'], response.json['data']['tenderPeriod']['endDate']) self.assertGreater(response.json['data']['next_check'], item['auctionPeriod']['startDate']) self.assertEqual(response.json['data']['next_check'], self.db.get(self.auction_id)['next_check']) if self.initial_lots: response = self.app.patch_json('/auctions/{}'.format(self.auction_id), {'data': {"lots": [{"auctionPeriod": {"startDate": response.json['data']['tenderPeriod']['endDate']}}]}}) item = response.json['data']["lots"][0] else: response = self.app.patch_json('/auctions/{}'.format(self.auction_id), {'data': {"auctionPeriod": {"startDate": response.json['data']['tenderPeriod']['endDate']}}}) item = response.json['data'] self.assertEqual(response.status, '200 OK') self.assertEqual(response.json['data']["status"], 'active.auction') self.assertGreaterEqual(item['auctionPeriod']['shouldStartAfter'], response.json['data']['tenderPeriod']['endDate']) self.assertNotIn('9999-01-01T00:00:00', item['auctionPeriod']['startDate']) self.assertGreater(response.json['data']['next_check'], response.json['data']['tenderPeriod']['endDate']) auction = self.db.get(self.auction_id) self.assertGreater(auction['next_check'], response.json['data']['tenderPeriod']['endDate']) auction['tenderPeriod']['endDate'] = auction['tenderPeriod']['startDate'] auction['tenderPeriod']['startDate'] = (datetime.strptime(auction['tenderPeriod']['endDate'][:19], "%Y-%m-%dT%H:%M:%S") - timedelta(days=10)).isoformat() auction['enquiryPeriod']['startDate'] = auction['tenderPeriod']['startDate'] auction['enquiryPeriod']['endDate'] = auction['tenderPeriod']['endDate'] auction['rectificationPeriod']['startDate'] = (datetime.strptime(auction['tenderPeriod']['endDate'][:19], "%Y-%m-%dT%H:%M:%S") - timedelta(days=10)).isoformat() auction['rectificationPeriod']['endDate'] = (datetime.strptime(auction['tenderPeriod']['endDate'][:19], "%Y-%m-%dT%H:%M:%S") - timedelta(days=5)).isoformat() if self.initial_lots: auction['lots'][0]['auctionPeriod']['startDate'] = auction['tenderPeriod']['endDate'] else: auction['auctionPeriod']['startDate'] = auction['tenderPeriod']['endDate'] self.db.save(auction) response = self.app.patch_json('/auctions/{}'.format(self.auction_id), {'data': {'id': self.auction_id}}) if self.initial_lots: item = response.json['data']["lots"][0] else: item = response.json['data'] self.assertGreaterEqual(item['auctionPeriod']['shouldStartAfter'], response.json['data']['tenderPeriod']['endDate']) self.assertNotIn('next_check', response.json['data']) self.assertNotIn('next_check', self.db.get(self.auction_id)) shouldStartAfter = item['auctionPeriod']['shouldStartAfter'] response = self.app.patch_json('/auctions/{}'.format(self.auction_id), {'data': {'id': self.auction_id}}) if self.initial_lots: item = response.json['data']["lots"][0] else: item = response.json['data'] self.assertEqual(item['auctionPeriod']['shouldStartAfter'], shouldStartAfter) self.assertNotIn('next_check', response.json['data']) if self.initial_lots: response = self.app.patch_json('/auctions/{}'.format(self.auction_id), {'data': {"lots": [{"auctionPeriod": {"startDate": "9999-01-01T00:00:00"}}]}}) item = response.json['data']["lots"][0] else: response = self.app.patch_json('/auctions/{}'.format(self.auction_id), {'data': {"auctionPeriod": {"startDate": "9999-01-01T00:00:00"}}}) item = response.json['data'] self.assertEqual(response.status, '200 OK') self.assertEqual(response.json['data']["status"], 'active.auction') self.assertGreaterEqual(item['auctionPeriod']['shouldStartAfter'], response.json['data']['tenderPeriod']['endDate']) self.assertIn('9999-01-01T00:00:00', item['auctionPeriod']['startDate']) self.assertIn('9999-01-01T00:00:00', response.json['data']['next_check'])
<gh_stars>1-10 import os import numpy as np import tensorflow as tf import tensorflow.keras.backend as K import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from lib.ml_helpers import custom_loss, moa from lib.utils import array_trimmer class TestingGenerator(tf.keras.utils.Sequence): """ A generator to process testing data iteratively. """ def __init__(self, x_set, y_set, batch_size): self.x, self.y = x_set, y_set self.s0, self.s1 = self.x.shape[1], self.x.shape[2] self.batch_size = batch_size def __len__(self): return int(np.ceil(len(self.x) / self.batch_size)) def __getitem__(self, idx): batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size] batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size] return batch_x, batch_y class TrainingGenerator(TestingGenerator): """ A generator to feed training data iteratively, with augmentation. """ def __init__(self, x_set, y_set, batch_size): super().__init__(x_set, y_set, batch_size) @tf.function def data_augmentation(self, batch_x, batch_y): """ Augments input data by rolling an input pattern randomly and changing its relative intensity. """ def roll_drp(inputs): # (i) Roll reflectance pattern reflectance_patterns, eulers = inputs reflectance_patterns = tf.expand_dims(reflectance_patterns, axis=0) idx_shift = tf.random.uniform(shape=(1,), maxval=self.s1, dtype=tf.dtypes.int32) angular_shift = idx_shift / self.s1 * np.pi * 2 reflectance_patterns = tf.roll(reflectance_patterns, shift=idx_shift[0], axis=2) eulers = tf.stack(( tf.add(eulers[0], angular_shift[0]), eulers[1], eulers[2], ), axis=0) # (ii) Modify relative intensity based on normal distribution reflectance_patterns = tf.multiply(reflectance_patterns, tf.random.normal( shape=(1,), mean=1.0, stddev=0.2, dtype=tf.dtypes.float64)) reflectance_patterns = tf.clip_by_value(reflectance_patterns, 0, 255) reflectance_patterns = tf.squeeze(reflectance_patterns) return reflectance_patterns, eulers # Apply augmentation function and yield augmented batch batch_x, batch_y = tf.map_fn( fn=roll_drp, elems=(batch_x, batch_y), parallel_iterations=len(batch_x), ) return batch_x, batch_y def __getitem__(self, idx): batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size] batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size] batch_x, batch_y = self.data_augmentation(batch_x, batch_y) return batch_x, batch_y class CustomModel: def __init__(self, root, bs, test_size, s0, s1, checkpoint, train_samples, test_samples): self.bs = bs self.root = root self.s0, self.s1 = s0, s1 self.checkpoint = checkpoint self.total_epochs = 0 self.lr = 0 # Get training dataset self.training_dico, self.xtr, self.ytr = self.get_dataset( train_samples, subset='training_sets') # Keep a fraction of the data as validation set self.xtr, self.xval, self.ytr, self.yval = train_test_split( self.xtr, self.ytr, test_size=test_size) # Trim away last batch self.xtr, self.xval, self.ytr, self.yval = array_trimmer( self.bs, self.xtr, self.xval, self.ytr, self.yval) # Get testing dataset self.testing_dico, self.xte, self.yte = self.get_dataset( test_samples, subset='evaluation_sets') self.xte, self.yte = array_trimmer(self.bs, self.xte, self.yte) print() print('Training data: ', self.xtr.shape, self.ytr.shape) print('Test data: ', self.xte.shape, self.yte.shape) self.validation_dataset = TestingGenerator(self.xval, self.yval, self.bs) self.testing_dataset = TestingGenerator(self.xte, self.yte, self.bs) self.kernel_neurons_1 = None self.kernel_neurons_2 = None self.dense_neurons_1 = None self.dense_neurons_2 = None self.model = None def get_dataset(self, sample_names, subset): """ Loads data from selected sample names. """ xtr = np.empty((0, self.s0, self.s1)) ytr = np.empty((0, 3)) big_d = {} for name in sample_names: path = os.path.join(self.root, f'{subset}/{name}.npy') dataset = np.load(path, allow_pickle=True).item() xtr_sample = dataset.get('xtr') ytr_sample = dataset.get('ytr') big_d[name] = (xtr_sample, ytr_sample) xtr = np.vstack((xtr, xtr_sample)) ytr = np.vstack((ytr, ytr_sample)) idx = np.arange(len(xtr)) np.random.shuffle(idx) xtr = xtr[idx] ytr = ytr[idx] return big_d, xtr, ytr def create_model(self, kernel_neurons_1=16, kenerl_neurons_2=16, dense_neurons_1=64, dense_neurons_2=32): self.kernel_neurons_1 = kernel_neurons_1 self.kernel_neurons_2 = kenerl_neurons_2 self.dense_neurons_1 = dense_neurons_1 self.dense_neurons_2 = dense_neurons_2 @tf.function def preprocess(x): """ Rescales input to the range 0-1. """ x = tf.multiply(x, 1 / 255.0) x = tf.expand_dims(x, axis=-1) return x inputs = tf.keras.Input(shape=(self.s0, self.s1)) # Preprocessing layer (rescales input to 0-1) lambds = tf.keras.layers.Lambda( lambda x: preprocess(x), output_shape=(self.s0, self.s1, 2))(inputs) # Define the output output = self.euler_net(lambds) # EulerNet # output = self.reference_model(lambds) # Jha et al. # Build the model self.model = tf.keras.Model(inputs=inputs, outputs=output) # Compile the model self._compile() print(self.model.summary()) def _compile(self): self.model.compile( optimizer=tf.keras.optimizers.Adam(), loss=custom_loss, metrics=[moa], ) def euler_net(self, lambds): """ Core EulerNet architecture. """ init = tf.keras.initializers.VarianceScaling(0.5) conv2d0 = tf.keras.layers.Conv2D(self.kernel_neurons_1, (3, 3), strides=(1, 1), padding="same", activation='relu')(lambds) mpool1 = tf.keras.layers.MaxPooling2D((2, 2))(conv2d0) conv2d1 = tf.keras.layers.Conv2D(self.kernel_neurons_2, (3, 3), strides=(1, 1), padding="same", activation='relu')(mpool1) mpool2 = tf.keras.layers.MaxPooling2D((2, 2))(conv2d1) globav = tf.keras.layers.Flatten()(mpool2) dense0 = tf.keras.layers.Dense(self.dense_neurons_1 * 3, activation='relu', kernel_initializer=init)(globav) dense01 = tf.keras.layers.Dense(self.dense_neurons_2 * 3, activation='relu', kernel_initializer=init)(dense0) output = tf.keras.layers.Dense(3, activation=None)(dense01) return output # def reference_model(self, lambds): # """ # Deeper model architecture - Does not lead to any improvement. # """ # init = tf.keras.initializers.VarianceScaling(0.5) # conv2d00 = tf.keras.layers.Conv2D(256, (3, 3), strides=(1, 1), padding="same", # activation='relu')(lambds) # conv2d01 = tf.keras.layers.Conv2D(256, (3, 3), strides=(1, 1), padding="same", # activation='relu')(conv2d00) # conv2d02 = tf.keras.layers.Conv2D(512, (3, 3), strides=(1, 1), padding="same", # activation='relu')(conv2d01) # conv2d03 = tf.keras.layers.Conv2D(512, (3, 3), strides=(1, 1), padding="same", # activation='relu')(conv2d02) # mpool1 = tf.keras.layers.MaxPooling2D((2, 2))(conv2d03) # conv2d10 = tf.keras.layers.Conv2D(256, (3, 3), strides=(1, 1), padding="same", # activation='relu')(mpool1) # conv2d11 = tf.keras.layers.Conv2D(256, (3, 3), strides=(1, 1), padding="same", # activation='relu')(conv2d10) # conv2d12 = tf.keras.layers.Conv2D(512, (3, 3), strides=(1, 1), padding="same", # activation='relu')(conv2d11) # conv2d13 = tf.keras.layers.Conv2D(512, (3, 3), strides=(1, 1), padding="same", # activation='relu')(conv2d12) # mpool2 = tf.keras.layers.MaxPooling2D((2, 2))(conv2d13) # globav = tf.keras.layers.Flatten()(mpool2) # dense0 = tf.keras.layers.Dense(512, activation='relu', kernel_initializer=init)(globav) # dense1 = tf.keras.layers.Dense(256, activation='relu', kernel_initializer=init)(dense0) # output = tf.keras.layers.Dense(3, activation=None)(dense1) # return output def run_lr_scheduler(self): """ Learning rate optimization between lr=1e-6 and 1e-2 (50 epochs) """ model = self.model training_dataset = TrainingGenerator( self.xtr, self.ytr, self.bs) lr_scheduler = tf.keras.callbacks.LearningRateScheduler( lambda epoch: 1e-5 * 10 ** (epoch / 10)) history = model.fit( training_dataset, epochs=40, verbose=1, validation_data=self.validation_dataset, callbacks=[lr_scheduler], ) lrs = history.history["lr"] losses = history.history["loss"] optimal_lr = lrs[np.argmin(losses)] fig, ax = plt.subplots(figsize=(8, 6), dpi=200) ax.semilogx(lrs, losses) ax.set_xlabel('learning rate') ax.set_ylabel('loss') ax.set_title('Optimal lr: {:.2e}'.format(optimal_lr)) plt.show() self.create_model( self.kernel_neurons_1, self.kernel_neurons_2, self.dense_neurons_1, self.dense_neurons_2, ) return optimal_lr # Optimal learning rate! def set_learning_rate(self, lr): K.set_value(self.model.optimizer.learning_rate, lr) print('\n> Set learning rate to: {:.2e}'.format(lr)) self.lr = lr def fit(self, patience=None, epochs=1, save_it=False): """ Fits the model, until validation stabilizes for [patience] epochs (if specified) or after a pre-determined number of epochs otherwise. """ callbacks = [] if patience is not None: early_stopping = tf.keras.callbacks.EarlyStopping( monitor='val_loss', patience=patience) callbacks.append(early_stopping) if save_it: cp_callback = tf.keras.callbacks.ModelCheckpoint( filepath=self.checkpoint, save_weights_only=True, verbose=1) callbacks.append(cp_callback) training_dataset = TrainingGenerator(self.xtr, self.ytr, self.bs) history = self.model.fit( training_dataset, epochs=epochs, verbose=1, validation_data=self.validation_dataset, callbacks=callbacks, ) fig, ax = plt.subplots(figsize=(16, 6)) for metric, values in history.history.items(): ax.plot(values, label=metric) ax.set_ylim(bottom=0) plt.legend() plt.show() self.total_epochs = len(history.history['loss']) print('FINISHED - TOTAL EPOCHS: ', self.total_epochs) def reload(self, checkpoint): """Reloads a model from checkpoint.""" self.model.load_weights(checkpoint).expect_partial() self._compile() print(f'\n> Reloaded model weights from: {checkpoint}') def predict(self, dataset): return self.model.predict(dataset) # def evaluate(self, on='test'): # """ # Computes MOA histograms and averages over training, val or test set. # """ # if on == 'training': # dataset = TestingGenerator(self.xtr, self.ytr, self.bs) # preds = self.predict(dataset) # moas, median_moa, ma_x, ma_y, ma_z = evaluate(preds, self.ytr) # elif on == 'test': # dataset = self.testing_dataset # preds = self.predict(dataset) # moas, median_moa, ma_x, ma_y, ma_z = evaluate(preds, self.yte) # else: # dataset = self.validation_dataset # preds = self.predict(dataset) # moas, median_moa, ma_x, ma_y, ma_z = evaluate(preds, self.yval) # return moas, median_moa, ma_x, ma_y, ma_z
#!/usr/bin/env python """ This code holds the solution for part 2 of day 4 of the Advent of Code for 2020. """ import re import sys def valid_birthday(value): byr = int(value) if byr >= 1920 and byr <= 2002: return True return False def valid_issue_year(value): iyr = int(value) if iyr >= 2010 and iyr <= 2020: return True return False def valid_expiry_year(value): eyr = int(value) if eyr >= 2020 and eyr <= 2030: return True return False def valid_height(value): try: height = int(value[:-2]) units = value[-2:] if units == 'cm': if height >= 150 and height <= 193: return True elif units == 'in': if height >= 59 and height <= 76: return True except: pass return False def valid_hair_colour(value): return re.match(r'#([0-9a-f]){6}', value) is not None def valid_eye_colour(value): valid = ["amb", "blu", "brn", "gry", "grn", "hzl", "oth"] if value in valid: return True return False def valid_passport_id(value): try: pid = int(value) if len(value) == 9: return True except: pass return False def check_passport(passport): expected = { 'byr': False, 'iyr': False, 'eyr': False, 'hgt': False, 'hcl': False, 'ecl': False, 'pid': False, # 'cid': False } codes = " ".join(passport).split(' ') for code_str in codes: code, value = code_str.split(':') if code in expected: if code == 'byr': if not valid_birthday(value): continue elif code == 'iyr': if not valid_issue_year(value): continue elif code == 'eyr': if not valid_expiry_year(value): continue elif code == 'hgt': if not valid_height(value): continue elif code == 'hcl': if not valid_hair_colour(value): continue if code == 'ecl': if not valid_eye_colour(value): continue if code == 'pid': if not valid_passport_id(value): continue expected[code] = True for code in expected: if not expected[code]: return False return True def calculate_solution(passports): count = 0 passport = [] for line in passports: if line != '': passport.append(line) else: if check_passport(passport): count += 1 passport = [] if passport != []: if check_passport(passport): count += 1 passport = [] return count def run_test(test_input, expected_solution): """ Helper method for running some unit tests whilst minimising repetative code. """ result = calculate_solution(test_input) if result != expected_solution: print("Test for input {0} FAILED. Got a result of {1}, not {2}".format(test_input, result, expected_solution)) sys.exit(-1) print("Test for input {0} passed.".format(test_input)) return result # Run any tests that we've defined to help validate our code prior to # trying to solve the puzzle. assert valid_birthday("1919") == False assert valid_birthday("1920") == True assert valid_birthday("2002") == True assert valid_birthday("2003") == False assert valid_issue_year("2009") == False assert valid_issue_year("2010") == True assert valid_issue_year("2020") == True assert valid_issue_year("2021") == False assert valid_expiry_year("2019") == False assert valid_expiry_year("2020") == True assert valid_expiry_year("2030") == True assert valid_expiry_year("2031") == False assert valid_height("1in") == False assert valid_height("58in") == False assert valid_height("59in") == True assert valid_height("69in") == True assert valid_height("76in") == True assert valid_height("77in") == False assert valid_height("1cm") == False assert valid_height("10cm") == False assert valid_height("149cm") == False assert valid_height("150cm") == True assert valid_height("190cm") == True assert valid_height("193cm") == True assert valid_height("194cm") == False assert valid_height("190in") == False assert valid_height("190") == False assert valid_height("") == False assert valid_height("monkeys") == False assert valid_height("34monkeys") == False assert valid_height("193mn") == False assert valid_hair_colour("#123abc") == True assert valid_hair_colour("#123abz") == False assert valid_hair_colour("#123abz") == False assert valid_hair_colour("#000000") == True assert valid_hair_colour("#999999") == True assert valid_hair_colour("#aaaaaa") == True assert valid_hair_colour("#cccccc") == True assert valid_hair_colour("#dddddd") == True assert valid_hair_colour("#gggggg") == False assert valid_hair_colour("123abc") == False assert valid_hair_colour("#") == False assert valid_hair_colour("#0") == False assert valid_hair_colour("#00") == False assert valid_hair_colour("#000") == False assert valid_hair_colour("#0000") == False assert valid_hair_colour("#00000") == False assert valid_eye_colour("amb") == True assert valid_eye_colour("blu") == True assert valid_eye_colour("brn") == True assert valid_eye_colour("gry") == True assert valid_eye_colour("grn") == True assert valid_eye_colour("hzl") == True assert valid_eye_colour("oth") == True assert valid_eye_colour("wat") == False assert valid_eye_colour("aaa") == False assert valid_eye_colour("zzz") == False assert valid_eye_colour("") == False assert valid_passport_id("") == False assert valid_passport_id("0") == False assert valid_passport_id("00") == False assert valid_passport_id("000") == False assert valid_passport_id("0000") == False assert valid_passport_id("00000") == False assert valid_passport_id("000000") == False assert valid_passport_id("0000000") == False assert valid_passport_id("00000000") == False assert valid_passport_id("000000001") == True assert valid_passport_id("999999999") == True assert valid_passport_id("0123456789") == False assert valid_passport_id("abcdefghi") == False passport = [ "eyr:1972 cid:100", "hcl:#18171d ecl:amb hgt:170 pid:186cm iyr:2018 byr:1926", ] assert check_passport(passport) == False passport = [ "iyr:2019", "hcl:#602927 eyr:1967 hgt:170cm", "ecl:grn pid:012533040 byr:1946", ] assert check_passport(passport) == False passport = [ "hcl:dab227 iyr:2012", "ecl:brn hgt:182cm pid:021572410 eyr:2020 byr:1992 cid:277", ] assert check_passport(passport) == False passport = [ "hgt:59cm ecl:zzz", "eyr:2038 hcl:74454a iyr:2023", "pid:3556412378 byr:2007" ] assert check_passport(passport) == False passport = [ "pid:087499704 hgt:74in ecl:grn iyr:2012 eyr:2030 byr:1980", "hcl:#623a2f" ] assert check_passport(passport) == True passport = [ "eyr:2029 ecl:blu cid:129 byr:1989", "iyr:2014 pid:896056539 hcl:#a97842 hgt:165cm" ] assert check_passport(passport) == True passport = [ "hcl:#888785", "hgt:164cm byr:2001 iyr:2015 cid:88", "pid:545766238 ecl:hzl", "eyr:2022" ] assert check_passport(passport) == True passport = [ "iyr:2010 hgt:158cm hcl:#b6652a ecl:blu byr:1944 eyr:2021 pid:093154719" ] assert check_passport(passport) == True test_data = [ "eyr:1972 cid:100", "hcl:#18171d ecl:amb hgt:170 pid:186cm iyr:2018 byr:1926", "", "iyr:2019", "hcl:#602927 eyr:1967 hgt:170cm", "ecl:grn pid:012533040 byr:1946", "", "hcl:dab227 iyr:2012", "ecl:brn hgt:182cm pid:021572410 eyr:2020 byr:1992 cid:277", "", "hgt:59cm ecl:zzz", "eyr:2038 hcl:74454a iyr:2023", "pid:3556412378 byr:2007", "", "pid:087499704 hgt:74in ecl:grn iyr:2012 eyr:2030 byr:1980", "hcl:#623a2f", "", "eyr:2029 ecl:blu cid:129 byr:1989", "iyr:2014 pid:896056539 hcl:#a97842 hgt:165cm", "", "hcl:#888785", "hgt:164cm byr:2001 iyr:2015 cid:88", "pid:545766238 ecl:hzl", "eyr:2022", "", "iyr:2010 hgt:158cm hcl:#b6652a ecl:blu byr:1944 eyr:2021 pid:093154719" ] result = run_test(test_data, 4) print("") print("-----------------") print("All Tests PASSED.") print("-----------------") print("") # Ok, so if we reach here, then we can be reasonably sure that the code # above is working correctly. Let's use the actual captcha now. with open("input.txt", "r") as f: input_data = [line.strip() for line in f] answer = calculate_solution(input_data) print("Solution is {0}".format(answer))
<reponame>claranet/cloud-deploy from mock import mock, MagicMock, call from commands.buildimage import Buildimage from tests.helpers import get_test_application, mocked_logger, LOG_FILE, void @mock.patch('commands.buildimage.lxd_is_available') @mock.patch('commands.buildimage.LXDImageBuilder') @mock.patch('commands.buildimage.AWSImageBuilder') @mock.patch('commands.buildimage.create_userdata_launchconfig_update_asg', new=lambda a, b, c, d, e: True) @mock.patch('commands.buildimage.touch_app_manifest', new=void) @mock.patch('commands.buildimage.log', new=mocked_logger) @mock.patch('ghost_tools.log', new=mocked_logger) @mock.patch('ghost_aws.log', new=mocked_logger) def test_buildimage_ami(awsimagebuilder_mock, lxdimagebuilder_mock, lxd_is_available_mock): """ Test AWS AMI basic ok """ # Set up mocks and variables test_app = get_test_application() worker = MagicMock() worker.app = test_app worker.log_file = LOG_FILE def assert_ok(status, message=None): assert status == "done", "Status is {} and not done : {}".format(status, message) worker.update_status = assert_ok lxd_is_available_mock.return_value = False # Launching command cmd = Buildimage(worker) cmd._update_app_ami = void cmd._aws_image_builder.start_builder.return_value = "ami_id", "ami_name" cmd.execute() assert awsimagebuilder_mock.call_count == 1 assert lxdimagebuilder_mock.call_count == 0 @mock.patch('commands.buildimage.lxd_is_available') @mock.patch('commands.buildimage.LXDImageBuilder') @mock.patch('commands.buildimage.AWSImageBuilder') @mock.patch('commands.buildimage.create_userdata_launchconfig_update_asg', new=lambda a, b, c, d, e: True) @mock.patch('commands.buildimage.touch_app_manifest', new=void) @mock.patch('commands.buildimage.log', new=mocked_logger) @mock.patch('ghost_tools.log', new=mocked_logger) @mock.patch('ghost_aws.log', new=mocked_logger) def test_buildimage_ami_error(awsimagebuilder_mock, lxdimagebuilder_mock, lxd_is_available_mock): """ Test build AWS AMI is failed """ # Set up mocks and variables test_app = get_test_application() worker = MagicMock() worker.app = test_app worker.log_file = LOG_FILE def assert_failed(status, message=None): assert status == "failed", "Status is {} and not failed: {}".format(status, message) worker.update_status = assert_failed lxd_is_available_mock.return_value = False # Launching command cmd = Buildimage(worker) cmd._update_app_ami = void cmd._aws_image_builder.start_builder.return_value = "ERROR", "ERROR" cmd.execute() assert awsimagebuilder_mock.call_count == 1 assert lxdimagebuilder_mock.call_count == 0 @mock.patch('commands.buildimage.lxd_is_available') @mock.patch('commands.buildimage.LXDImageBuilder') @mock.patch('commands.buildimage.AWSImageBuilder') @mock.patch('commands.buildimage.create_userdata_launchconfig_update_asg', new=lambda a, b, c, d, e: True) @mock.patch('commands.buildimage.touch_app_manifest', new=void) @mock.patch('commands.buildimage.log', new=mocked_logger) @mock.patch('ghost_tools.log', new=mocked_logger) @mock.patch('ghost_aws.log', new=mocked_logger) def test_buildimage_ami_error_with_lxd(awsimagebuilder_mock, lxdimagebuilder_mock, lxd_is_available_mock): """ Test build AWS AMI is failed """ # Set up mocks and variables test_app = get_test_application() worker = MagicMock() worker.app = test_app worker.log_file = LOG_FILE def assert_failed(status, message=None): assert status == "failed", "Status is {} and not failed: {}".format(status, message) worker.update_status = assert_failed lxd_is_available_mock.return_value = True # Launching command cmd = Buildimage(worker) cmd._update_app_ami = void cmd._update_container_source = void cmd._aws_image_builder.start_builder.return_value = "ERROR", "ERROR" cmd.execute() assert awsimagebuilder_mock.call_count == 1 assert lxdimagebuilder_mock.call_count == 1 @mock.patch('commands.buildimage.lxd_is_available') @mock.patch('commands.buildimage.LXDImageBuilder') @mock.patch('commands.buildimage.AWSImageBuilder') @mock.patch('commands.buildimage.create_userdata_launchconfig_update_asg', new=lambda a, b, c, d, e: True) @mock.patch('commands.buildimage.touch_app_manifest', new=void) @mock.patch('commands.buildimage.log', new=mocked_logger) @mock.patch('ghost_tools.log', new=mocked_logger) @mock.patch('ghost_aws.log', new=mocked_logger) def test_buildimage_lxd(AWSImageBuilder_mock, LXDImageBuilder_mock, lxd_is_available_mock): """ Test LXD Image Build """ # Set up mocks and variables test_app = get_test_application() test_app['build_infos']['source_container_image'] = 'dummy_lxc_source_image' worker = MagicMock() worker.app = test_app worker.log_file = LOG_FILE def assert_ok(status, message=None): assert status == "done", "Status is {} and not done : {}".format(status, message) worker.update_status = assert_ok lxd_is_available_mock.return_value = True # Launching command cmd = Buildimage(worker) cmd._update_app_ami = void cmd._update_container_source = void cmd._aws_image_builder.start_builder.return_value = "ami_id", "ami_name" cmd.execute() assert AWSImageBuilder_mock.call_count == 1 assert LXDImageBuilder_mock.call_count == 1
<reponame>VITA-Group/AugMax<gh_stars>10-100 ''' Tiny-ImageNet: Download by wget http://cs231n.stanford.edu/tiny-imagenet-200.zip Run python create_tin_val_folder.py to construct the validation set. Tiny-ImageNet-C: Download by wget https://zenodo.org/record/2469796/files/TinyImageNet-C.tar?download=1 Run python dataloaders/fix_tin_c.py to remove the redundant images in TIN-C. Tiny-ImageNet-V2: Download ImageNet-V2 from http://imagenetv2public.s3-website-us-west-2.amazonaws.com/ Run python dataloaders/construct_tin_v2.py to select 200-classes from the full ImageNet-V2 dataset. https://github.com/snu-mllab/PuzzleMix/blob/master/load_data.py ''' import torch from torch.utils.data import Subset, DataLoader from torchvision import datasets from torchvision import transforms import numpy as np import os, shutil def tiny_imagenet_dataloaders(data_dir, transform_train=True, AugMax=None, **AugMax_args): train_root = os.path.join(data_dir, 'train') # this is path to training images folder validation_root = os.path.join(data_dir, 'val/images') # this is path to validation images folder print('Training images loading from %s' % train_root) print('Validation images loading from %s' % validation_root) if AugMax is not None: train_transform = transforms.Compose( [transforms.RandomHorizontalFlip(), transforms.RandomCrop(64, padding=4)]) if transform_train else None test_transform = transforms.Compose([transforms.ToTensor()]) train_data = datasets.ImageFolder(train_root, transform=train_transform) test_data = datasets.ImageFolder(validation_root, transform=test_transform) train_data = AugMax(train_data, test_transform, mixture_width=AugMax_args['mixture_width'], mixture_depth=AugMax_args['mixture_depth'], aug_severity=AugMax_args['aug_severity'], ) else: train_transform = transforms.Compose( [transforms.RandomHorizontalFlip(), transforms.RandomCrop(64, padding=4), transforms.ToTensor()]) if transform_train else transforms.Compose([transforms.ToTensor()]) test_transform = transforms.Compose([transforms.ToTensor()]) train_data = datasets.ImageFolder(train_root, transform=train_transform) test_data = datasets.ImageFolder(validation_root, transform=test_transform) return train_data, test_data def tiny_imagenet_deepaug_dataloaders(data_dir, transform_train=True, AugMax=None, **AugMax_args): train_root = os.path.join(data_dir, 'train') # this is path to training images folder print('Training images loading from %s' % train_root) if AugMax is not None: train_transform = transforms.Compose( [transforms.RandomHorizontalFlip(), transforms.RandomCrop(64, padding=4)]) if transform_train else None test_transform = transforms.Compose([transforms.ToTensor()]) train_data = datasets.ImageFolder(train_root, transform=train_transform) train_data = AugMax(train_data, test_transform, mixture_width=AugMax_args['mixture_width'], mixture_depth=AugMax_args['mixture_depth'], aug_severity=AugMax_args['aug_severity'], ) else: train_transform = transforms.Compose( [transforms.RandomHorizontalFlip(), transforms.RandomCrop(64, padding=4), transforms.ToTensor()]) if transform_train else transforms.Compose([transforms.ToTensor()]) train_data = datasets.ImageFolder(train_root, transform=train_transform) return train_data def tiny_imagenet_c_testloader(data_dir, corruption, severity, test_batch_size=1000, num_workers=4): test_transform = transforms.Compose([transforms.ToTensor()]) test_root = os.path.join(data_dir, corruption, str(severity)) test_c_data = datasets.ImageFolder(test_root,transform=test_transform) test_c_loader = DataLoader(test_c_data, batch_size=test_batch_size, shuffle=False, num_workers=num_workers, pin_memory=True) return test_c_loader
<gh_stars>10-100 import os import dj_database_url from dotenv import load_dotenv from pathlib import Path from urllib.parse import urlparse load_dotenv() # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent SECRET_KEY = os.getenv('DJANGO_SECRET_KEY', 'django-insecure') DEBUG = bool(os.getenv('DEBUG', False)) ALLOWED_HOSTS = ['*'] USE_X_FORWARDED_HOST = True SECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https') # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'rest_framework', 'social_django', 'corsheaders', 'tabby.app', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'whitenoise.middleware.WhiteNoiseMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', 'corsheaders.middleware.CorsMiddleware', 'tabby.middleware.TokenMiddleware', 'tabby.middleware.GAMiddleware', ] ROOT_URLCONF = 'tabby.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'tabby.wsgi.application' DATABASES = { 'default': dj_database_url.config(conn_max_age=600) } CACHES = { 'default': { 'BACKEND': 'django.core.cache.backends.locmem.LocMemCache', } } AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] AUTH_USER_MODEL = 'app.User' REST_FRAMEWORK = { 'DEFAULT_RENDERER_CLASSES': ( 'rest_framework.renderers.JSONRenderer', ) } LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True LOGGING = { 'version': 1, 'disable_existing_loggers': False, 'formatters': { 'simple': { 'format': '%(levelname)s %(message)s' }, }, 'handlers': { 'console': { 'level': 'INFO', 'class': 'logging.StreamHandler', 'formatter': 'simple' }, }, 'loggers': { '': { 'handlers': ['console'], 'propagate': False, 'level': 'INFO', }, }, } DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField' CSRF_USE_SESSIONS = False CSRF_COOKIE_HTTPONLY = False CSRF_COOKIE_NAME = 'XSRF-TOKEN' CSRF_HEADER_NAME = 'HTTP_X_XSRF_TOKEN' AUTHENTICATION_BACKENDS = ( 'social_core.backends.github.GithubOAuth2', 'social_core.backends.gitlab.GitLabOAuth2', 'social_core.backends.azuread.AzureADOAuth2', 'social_core.backends.microsoft.MicrosoftOAuth2', 'social_core.backends.google.GoogleOAuth2', 'django.contrib.auth.backends.ModelBackend', ) SOCIAL_AUTH_GITHUB_SCOPE = ['read:user', 'user:email'] SOCIAL_AUTH_PIPELINE = ( 'social_core.pipeline.social_auth.social_details', 'social_core.pipeline.social_auth.social_uid', 'social_core.pipeline.social_auth.auth_allowed', 'social_core.pipeline.social_auth.social_user', 'social_core.pipeline.user.get_username', 'social_core.pipeline.social_auth.associate_by_email', 'social_core.pipeline.user.create_user', 'social_core.pipeline.social_auth.associate_user', 'social_core.pipeline.social_auth.load_extra_data', 'social_core.pipeline.user.user_details', ) APP_DIST_STORAGE = os.getenv('APP_DIST_STORAGE', 'file://' + str(BASE_DIR / 'app-dist')) NPM_REGISTRY = os.getenv('NPM_REGISTRY', 'https://registry.npmjs.org').rstrip('/') FRONTEND_BUILD_DIR = Path(os.getenv('FRONTEND_BUILD_DIR', BASE_DIR / '../frontend/build')) FRONTEND_URL = None BACKEND_URL = None GITHUB_ELIGIBLE_SPONSORSHIPS = None for key in [ 'FRONTEND_URL', 'BACKEND_URL', 'SOCIAL_AUTH_GITHUB_KEY', 'SOCIAL_AUTH_GITHUB_SECRET', 'SOCIAL_AUTH_GITLAB_KEY', 'SOCIAL_AUTH_GITLAB_SECRET', 'SOCIAL_AUTH_GOOGLE_OAUTH2_KEY', 'SOCIAL_AUTH_GOOGLE_OAUTH2_SECRET', 'SOCIAL_AUTH_MICROSOFT_GRAPH_KEY', 'SOCIAL_AUTH_MICROSOFT_GRAPH_SECRET', 'CONNECTION_GATEWAY_AUTH_CA', 'CONNECTION_GATEWAY_AUTH_CERTIFICATE', 'CONNECTION_GATEWAY_AUTH_KEY', 'GITHUB_ELIGIBLE_SPONSORSHIPS', 'GITHUB_SPONSORS_MIN_PAYMENT', 'GA_ID', 'GA_DOMAIN', 'ENABLE_LOGIN', ]: globals()[key] = os.getenv(key) for key in [ 'GITHUB_SPONSORS_MIN_PAYMENT', ]: globals()[key] = int(globals()[key]) if globals()[key] else None for key in [ 'CONNECTION_GATEWAY_AUTH_CA', 'CONNECTION_GATEWAY_AUTH_CERTIFICATE', 'CONNECTION_GATEWAY_AUTH_KEY', ]: v = globals()[key] if v and not os.path.exists(v): raise ValueError(f'{v} does not exist') if GITHUB_ELIGIBLE_SPONSORSHIPS: GITHUB_ELIGIBLE_SPONSORSHIPS = GITHUB_ELIGIBLE_SPONSORSHIPS.split(',') else: GITHUB_ELIGIBLE_SPONSORSHIPS = [] STATIC_URL = '/static/' if FRONTEND_BUILD_DIR.exists(): STATICFILES_DIRS = [FRONTEND_BUILD_DIR] STATIC_ROOT = BASE_DIR / 'public' if FRONTEND_URL: CORS_ALLOWED_ORIGINS = [FRONTEND_URL, 'https://tabby.sh'] CORS_ALLOW_CREDENTIALS = True CORS_ALLOW_HEADERS = [ 'accept', 'accept-encoding', 'authorization', 'content-type', 'dnt', 'origin', 'user-agent', 'x-xsrf-token', 'x-requested-with', ] frontend_domain = urlparse(FRONTEND_URL).hostname CSRF_TRUSTED_ORIGINS = [frontend_domain] if BACKEND_URL: CSRF_TRUSTED_ORIGINS.append(urlparse(BACKEND_URL).hostname) SESSION_COOKIE_DOMAIN = os.getenv('SESSION_COOKIE_DOMAIN', frontend_domain) SESSION_COOKIE_SAMESITE = None CSRF_COOKIE_DOMAIN = frontend_domain FRONTEND_URL = FRONTEND_URL.rstrip('/') if FRONTEND_URL.startswith('https://'): CSRF_COOKIE_SECURE = True SESSION_COOKIE_SECURE = True else: FRONTEND_URL = '' LOGIN_REDIRECT_URL = FRONTEND_URL + '/app'
<reponame>moreati/python-niceware # flake8: noqa E122 # 2^16 English wordlist. Derived from # https://github.com/diracdeltas/niceware/blob/master/lib/wordlist.js # which in turn, is derived from # http://www-01.sil.org/linguistics/wordlists/english/. # Originally compiled for the Yahoo End-to-End project. # https://github.com/yahoo/end-to-end WORD_LIST = [ 'a', 'aah', 'aardvark', 'aardwolf', 'academia', 'academic', 'academical', 'academician', 'academicianship', 'academicism', 'academy', 'acadia', 'acapulco', 'ace', 'aced', 'acerb', 'acerbate', 'acerber', 'acerbest', 'acerbic', 'acerbity', 'acerola', 'acerose', 'acetate', 'acetic', 'acetified', 'acetify', 'acetifying', 'acetone', 'acetonic', 'ache', 'ached', 'achene', 'achenial', 'achier', 'achiest', 'achievable', 'achieve', 'achieved', 'achievement', 'achiever', 'achieving', 'aching', 'achoo', 'achordate', 'achromat', 'achromatic', 'achromatism', 'achy', 'acid', 'acidhead', 'acidic', 'acidifiable', 'acidification', 'acidified', 'acidifier', 'acidify', 'acidifying', 'acidity', 'acidly', 'acidotic', 'acidulate', 'acidulation', 'acidulously', 'acidy', 'acing', 'acknowledge', 'acknowledgeable', 'acknowledgement', 'acknowledger', 'acknowledging', 'acknowledgment', 'aclu', 'acme', 'acne', 'acned', 'acoin', 'acolyte', 'aconite', 'acorn', 'acoustic', 'acoustical', 'acquaint', 'acquaintance', 'acquaintanceship', 'acquainted', 'acquainting', 'acquiesce', 'acquiesced', 'acquiescence', 'acquiescent', 'acquiescently', 'acquiescing', 'acquiesence', 'acquirable', 'acquire', 'acquirement', 'acquirer', 'acquiring', 'acquisition', 'acquisitive', 'acquit', 'acquittal', 'acquitted', 'acquitter', 'acquitting', 'acre', 'acreage', 'acrid', 'acrider', 'acridest', 'acridity', 'acridly', 'acrimoniously', 'acrimony', 'acrobat', 'acrobatic', 'acromegalic', 'acromegaly', 'acronym', 'acrophobia', 'acrostic', 'acrylate', 'acrylic', 'act', 'actable', 'acted', 'actin', 'acting', 'actinic', 'actinide', 'actinism', 'actinium', 'action', 'actionability', 'actionable', 'activate', 'activation', 'active', 'activism', 'activist', 'activistic', 'activity', 'actomyosin', 'actorish', 'actual', 'actuality', 'actualization', 'actualize', 'actualized', 'actualizing', 'actuarial', 'actuary', 'actuate', 'actuation', 'acuity', 'acupuncture', 'acupuncturist', 'acute', 'acutely', 'acuter', 'acutest', 'ad', 'adage', 'adagial', 'adagio', 'adam', 'adamance', 'adamancy', 'adamant', 'adamantine', 'adamantly', 'adapt', 'adaptability', 'adaptable', 'adaptation', 'adapted', 'adapter', 'adapting', 'adaption', 'adaptive', 'adaptometer', 'adhere', 'adherence', 'adherent', 'adherer', 'adhering', 'adhesion', 'adhesional', 'adhesive', 'adiabatic', 'adiathermancy', 'adieu', 'adieux', 'adipose', 'adiposity', 'adjacency', 'adjacent', 'adjacently', 'adjectival', 'adjective', 'adjoin', 'adjoined', 'adjoining', 'adjoint', 'adjourn', 'adjourned', 'adjourning', 'adjournment', 'adjudge', 'adjudging', 'adjudicate', 'adjudication', 'adjunct', 'adjunctive', 'adjunctly', 'adjuration', 'adjuratory', 'adjure', 'adjurer', 'adjuring', 'adjuror', 'adjust', 'adjustable', 'adjusted', 'adjuster', 'adjusting', 'adjustment', 'adjutancy', 'adjutant', 'admin', 'administer', 'administerial', 'administering', 'administrable', 'administrant', 'administrate', 'administration', 'administrational', 'administrative', 'administratrix', 'adminstration', 'admirable', 'admirably', 'admiral', 'admiralship', 'admiralty', 'admiration', 'admire', 'admirer', 'admiring', 'admissability', 'admissable', 'admissibility', 'admissible', 'admissibly', 'admission', 'admissive', 'admit', 'admittance', 'admitted', 'admitter', 'admitting', 'admonish', 'admonished', 'admonisher', 'admonishing', 'admonishment', 'admonition', 'admonitory', 'ado', 'adobe', 'adolescence', 'adolescent', 'adolescently', 'adolf', 'adolph', 'adopt', 'adoptability', 'adoptable', 'adopted', 'adoptee', 'adopter', 'adopting', 'adoption', 'adoptive', 'adorability', 'adorable', 'adorably', 'adoration', 'adore', 'adorer', 'adoring', 'adorn', 'adorned', 'adorner', 'adorning', 'adornment', 'adoze', 'adrenal', 'adrenalin', 'adrenaline', 'adrenocortical', 'adriatic', 'adrift', 'adroit', 'adroiter', 'adroitest', 'adroitly', 'adsorb', 'adsorbable', 'adsorbate', 'adsorbed', 'adsorbent', 'adsorbing', 'adsorption', 'adsorptive', 'adulate', 'adulation', 'adulatory', 'adult', 'adulterant', 'adulterate', 'adulteration', 'adulterer', 'adulterously', 'adultery', 'adulthood', 'adultly', 'adumbrate', 'adumbration', 'adumbrative', 'advance', 'advanced', 'advancement', 'advancer', 'advancing', 'advantage', 'advantageously', 'advantaging', 'advent', 'adventitiously', 'adventure', 'adventurer', 'adventuresome', 'adventuring', 'adventurously', 'adverb', 'adverbial', 'adversary', 'adversative', 'adverse', 'adversely', 'adversity', 'advert', 'adverted', 'advertent', 'advertently', 'adverting', 'advertise', 'advertised', 'advertisement', 'advertiser', 'advertising', 'advertize', 'advertized', 'advertizement', 'advertizer', 'advertizing', 'advice', 'advisability', 'advisable', 'advisatory', 'advise', 'advised', 'advisee', 'advisement', 'adviser', 'advising', 'advisor', 'advisory', 'advocacy', 'advocate', 'advocatory', 'aelurophobia', 'aeolian', 'aeon', 'aeonian', 'aeonic', 'aerate', 'aeration', 'aerial', 'aerialist', 'aerobic', 'aerobiology', 'aerodrome', 'aerodynamic', 'aerodynamical', 'aerodyne', 'aerofoil', 'aerogram', 'aerolite', 'aerolith', 'aerological', 'aerologist', 'aerology', 'aerometer', 'aeronaut', 'aeronautic', 'aeronautical', 'aerophobia', 'aeroplane', 'aerosol', 'aerospace', 'aerostat', 'aesop', 'aesopian', 'aesthesia', 'aesthete', 'aesthetic', 'aestivate', 'aether', 'aetheric', 'afar', 'afeard', 'affability', 'affable', 'affably', 'affair', 'affaire', 'affect', 'affectation', 'affected', 'affecter', 'affecting', 'affection', 'affectionate', 'affectionately', 'affective', 'affectivity', 'afferent', 'afferently', 'affiance', 'affianced', 'affiancing', 'affiant', 'affidavit', 'affiliate', 'affiliation', 'affinity', 'affirm', 'affirmable', 'affirmably', 'affirmance', 'affirmation', 'affirmative', 'affirmed', 'affirmer', 'affirming', 'affix', 'affixal', 'affixation', 'affixed', 'affixer', 'affixing', 'affixion', 'afflict', 'afflicted', 'afflicting', 'affliction', 'afflictive', 'affluence', 'affluent', 'affluently', 'afflux', 'afford', 'affordable', 'affording', 'afforest', 'afforestation', 'afforested', 'afforesting', 'affray', 'affrayed', 'affrayer', 'affraying', 'affright', 'affrighted', 'affront', 'affronted', 'affronting', 'afghan', 'afghani', 'afghanistan', 'aficionado', 'afield', 'afire', 'aflame', 'afloat', 'aflutter', 'afoot', 'afore', 'aforesaid', 'aforethought', 'afoul', 'afraid', 'afreet', 'afresh', 'africa', 'african', 'afrit', 'afro', 'aft', 'after', 'afterbirth', 'afterburner', 'aftercare', 'afterdeck', 'afterdischarge', 'aftereffect', 'afterglow', 'afterimage', 'afterimpression', 'afterlife', 'aftermarket', 'aftermath', 'aftermost', 'afternoon', 'afterpotential', 'aftershave', 'aftertaste', 'afterthought', 'afterward', 'aftmost', 'ah', 'aha', 'ahead', 'ahem', 'ahimsa', 'ahold', 'ahorse', 'ahoy', 'aid', 'aide', 'aider', 'aidful', 'aiding', 'aidman', 'aikido', 'ail', 'ailed', 'aileron', 'ailing', 'ailment', 'ailurophobe', 'ailurophobia', 'aim', 'aimed', 'aimer', 'aimful', 'aimfully', 'aiming', 'aimlessly', 'air', 'airbill', 'airboat', 'airborne', 'airbrush', 'airbrushed', 'airbrushing', 'aircraft', 'aircrew', 'airdrome', 'airdrop', 'airdropping', 'airedale', 'airer', 'airest', 'airfare', 'airfield', 'airflow', 'airfoil', 'airframe', 'airfreight', 'airglow', 'airhead', 'airier', 'airiest', 'airily', 'airing', 'airlessly', 'airlift', 'airlifted', 'airlifting', 'airlike', 'airline', 'airliner', 'airlock', 'airmail', 'airmailed', 'airmailing', 'airman', 'airmanship', 'airmobile', 'airplane', 'airport', 'airproofed', 'airscrew', 'airship', 'airsick', 'airspace', 'airspeed', 'airstream', 'airstrip', 'airtight', 'airwave', 'airway', 'airwoman', 'airworthier', 'airworthiest', 'airworthy', 'airy', 'aisle', 'aisled', 'aitch', 'ajar', 'ajiva', 'akimbo', 'akin', 'akron', 'akvavit', 'al', 'alabama', 'alabamian', 'alabaster', 'alack', 'alacrity', 'aladdin', 'alai', 'alameda', 'alamo', 'alamode', 'alan', 'alar', 'alarm', 'alarmclock', 'alarmed', 'alarming', 'alarmism', 'alarmist', 'alarum', 'alarumed', 'alaruming', 'alary', 'alaska', 'alaskan', 'alate', 'alba', 'albacore', 'albania', 'albanian', 'albany', 'albedo', 'albeit', 'albert', 'alberta', 'albinism', 'albino', 'albinoism', 'album', 'albumin', 'albuquerque', 'alcalde', 'alcazar', 'alchemic', 'alchemical', 'alchemist', 'alchemy', 'alcohol', 'alcoholic', 'alcoholism', 'alcoholization', 'alcoholized', 'alcoholizing', 'alcoholometer', 'alcove', 'alcoved', 'aldehyde', 'alder', 'alderman', 'aldermanic', 'aldermanry', 'alderwoman', 'aldrin', 'ale', 'aleatory', 'alee', 'alehouse', 'alembic', 'aleph', 'alert', 'alerted', 'alerter', 'alertest', 'alerting', 'alertly', 'aleuron', 'aleutian', 'alewife', 'alexander', 'alexandria', 'alexandrian', 'alexandrine', 'alexia', 'alfa', 'alfalfa', 'alfresco', 'alga', 'algae', 'algal', 'algebra', 'algebraic', 'algeria', 'algerian', 'algicide', 'algid', 'algin', 'alginate', 'algoid', 'algonquian', 'algonquin', 'algorism', 'algorithm', 'algorithmic', 'alibi', 'alibied', 'alice', 'alien', 'alienability', 'alienable', 'alienage', 'alienate', 'alienation', 'aliened', 'alienee', 'aliener', 'aliening', 'alienism', 'alienist', 'alienly', 'alight', 'alighted', 'alighting', 'align', 'aligned', 'aligner', 'aligning', 'alignment', 'alike', 'aliment', 'alimentary', 'alimentation', 'alimented', 'alimenting', 'alimony', 'aline', 'alined', 'alinement', 'aliner', 'alining', 'aliphatic', 'aliquant', 'aliquot', 'alit', 'aliter', 'alive', 'alizarin', 'alizarine', 'alkali', 'alkalic', 'alkalify', 'alkalin', 'alkaline', 'alkalinity', 'alkalinization', 'alkalinize', 'alkalinized', 'alkalinizing', 'alkalise', 'alkalization', 'alkalize', 'alkalized', 'alkalizing', 'alkaloid', 'alkyd', 'alkyl', 'all', 'allah', 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'winched', 'wincher', 'winching', 'wincing', 'wind', 'windable', 'windage', 'windbag', 'windblown', 'windbreak', 'windburn', 'windburned', 'windburnt', 'windchill', 'winder', 'windfall', 'windflower', 'windier', 'windiest', 'windily', 'winding', 'windjammer', 'windlassed', 'windmill', 'windmilled', 'window', 'windowed', 'windowing', 'windowpane', 'windowsill', 'windpipe', 'windproof', 'windrow', 'windrowing', 'windscreen', 'windshield', 'windsock', 'windsor', 'windstorm', 'windsurf', 'windswept', 'windup', 'windward', 'windy', 'wine', 'wined', 'winegrower', 'winery', 'wineshop', 'wineskin', 'winesop', 'winey', 'wing', 'wingback', 'wingding', 'wingier', 'winging', 'winglet', 'wingman', 'wingover', 'wingspan', 'wingspread', 'wingy', 'winier', 'winiest', 'wining', 'winish', 'wink', 'winked', 'winker', 'winking', 'winkle', 'winkled', 'winkling', 'winnable', 'winned', 'winner', 'winning', 'winnipeg', 'winnow', 'winnowed', 'winnower', 'winnowing', 'wino', 'winslow', 'winsome', 'winsomely', 'winsomer', 'winsomest', 'winter', 'winterer', 'wintergreen', 'winterier', 'winteriest', 'wintering', 'winterization', 'winterize', 'winterized', 'winterizing', 'winterkill', 'winterkilled', 'winterkilling', 'winterly', 'wintertide', 'wintertime', 'wintery', 'wintling', 'wintrier', 'wintriest', 'wintrily', 'wintry', 'winy', 'wipe', 'wiped', 'wipeout', 'wiper', 'wiping', 'wirable', 'wire', 'wiredraw', 'wiredrawn', 'wiredrew', 'wirehair', 'wirelessed', 'wireman', 'wirephoto', 'wirepuller', 'wirepulling', 'wirer', 'wiretap', 'wiretapper', 'wiretapping', 'wireway', 'wirework', 'wireworm', 'wirier', 'wiriest', 'wirily', 'wiring', 'wiry', 'wisconsin', 'wisconsinite', 'wisdom', 'wise', 'wiseacre', 'wisecrack', 'wisecracker', 'wisecracking', 'wised', 'wiseliest', 'wisely', 'wiser', 'wisest', 'wish', 'wishbone', 'wished', 'wisher', 'wishful', 'wishfully', 'wishing', 'wishy', 'wising', 'wisp', 'wisped', 'wispier', 'wispiest', 'wispily', 'wisping', 'wispish', 'wispy', 'wisteria', 'wistful', 'wistfully', 'wisting', 'wit', 'witch', 'witchcraft', 'witched', 'witchery', 'witchier', 'witchiest', 'witching', 'witchy', 'with', 'withal', 'withdraw', 'withdrawable', 'withdrawal', 'withdrawer', 'withdrawing', 'withdrawn', 'withdrew', 'withe', 'withed', 'wither', 'witherer', 'withering', 'withheld', 'withhold', 'withholder', 'withholding', 'withier', 'within', 'withing', 'without', 'withstand', 'withstanding', 'withstood', 'withy', 'witlessly', 'witling', 'witnessable', 'witnessed', 'witnesser', 'witnessing', 'witted', 'witticism', 'wittier', 'wittiest', 'wittily', 'witting', 'witty', 'wive', 'wived', 'wiver', 'wivern', 'wiving', 'wiz', 'wizard', 'wizardly', 'wizardry', 'wizen', 'wizened', 'wizening', 'woad', 'woald', 'wobble', 'wobbled', 'wobbler', 'wobblier', 'wobbliest', 'wobbling', 'wobbly', 'wobegone', 'woe', 'woebegone', 'woeful', 'woefuller', 'woefullest', 'woefully', 'woesome', 'woful', 'wofully', 'wok', 'woke', 'woken', 'wold', 'wolf', 'wolfed', 'wolfer', 'wolfhound', 'wolfing', 'wolfish', 'wolfram', 'wolfsbane', 'wolver', 'wolverine', 'woman', 'womaned', 'womanhood', 'womanish', 'womanize', 'womanized', 'womanizer', 'womanizing', 'womankind', 'womanlier', 'womanliest', 'womanlike', 'womanly', 'womb', 'wombat', 'wombed', 'wombier', 'womby', 'womenfolk', 'won', 'wonder', 'wonderer', 'wonderful', 'wonderfully', 'wondering', 'wonderland', 'wonderment', 'wondrously', 'wonkier', 'wonky', 'wont', 'wonted', 'wonting', 'wonton', 'woo', 'wood', 'woodbin', 'woodbine', 'woodblock', 'woodbox', 'woodcarver', 'woodcarving', 'woodchopper', 'woodchuck', 'woodcock', 'woodcraft', 'woodcut', 'woodcutter', 'woodcutting', 'wooden', 'woodener', 'woodenest', 'woodenly', 'woodenware', 'woodgraining', 'woodhen', 'woodier', 'woodiest', 'wooding', 'woodland', 'woodlander', 'woodlore', 'woodlot', 'woodman', 'woodnote', 'woodpecker', 'woodpile', 'woodruff', 'woodshed', 'woodsier', 'woodsiest', 'woodsman', 'woodsy', 'woodward', 'woodwax', 'woodwind', 'woodwork', 'woodworker', 'woodworking', 'woodworm', 'woody', 'wooed', 'wooer', 'woof', 'woofed', 'woofer', 'woofing', 'wooing', 'wool', 'wooled', 'woolen', 'wooler', 'woolgathering', 'woolie', 'woolier', 'wooliest', 'woollen', 'woollier', 'woolliest', 'woolly', 'woolman', 'woolpack', 'woolsack', 'woolshed', 'woolskin', 'woolsorter', 'woolworth', 'wooly', 'woomera', 'woosh', 'wooshed', 'wooshing', 'woozier', 'wooziest', 'woozily', 'woozy', 'wop', 'worcester', 'word', 'wordage', 'wordbook', 'wordier', 'wordiest', 'wordily', 'wording', 'wordlessly', 'wordperfect', 'wordplay', 'wordstar', 'wordy', 'wore', 'work', 'workability', 'workable', 'workaday', 'workaholic', 'workaholism', 'workbag', 'workbench', 'workboat', 'workbook', 'workbox', 'workday', 'worked', 'worker', 'workfolk', 'workhand', 'workhorse', 'workhouse', 'working', 'workingman', 'workingwoman', 'workload', 'workman', 'workmanlike', 'workmanship', 'workmaster', 'workout', 'workroom', 'workshop', 'workstation', 'worktable', 'workup', 'workweek', 'workwoman', 'world', 'worldbeater', 'worldlier', 'worldliest', 'worldling', 'worldly', 'worldwide', 'worm', 'wormed', 'wormer', 'wormhole', 'wormier', 'wormiest', 'worming', 'wormish', 'wormwood', 'wormy', 'worn', 'wornout', 'worried', 'worrier', 'worriment', 'worrisome', 'worrisomely', 'worrit', 'worry', 'worrying', 'worrywart', 'worse', 'worsen', 'worsened', 'worsening', 'worser', 'worship', 'worshiped', 'worshiper', 'worshipful', 'worshipfully', 'worshiping', 'worshipper', 'worshipping', 'worst', 'worsted', 'worsting', 'wort', 'worth', 'worthed', 'worthful', 'worthier', 'worthiest', 'worthily', 'worthing', 'worthlessly', 'worthwhile', 'worthy', 'wotted', 'wotting', 'would', 'wouldest', 'wouldst', 'wound', 'wounding', 'wove', 'woven', 'wow', 'wowed', 'wowing', 'wowser', 'wrack', 'wrackful', 'wracking', 'wraith', 'wrang', 'wrangle', 'wrangled', 'wrangler', 'wrangling', 'wrap', 'wraparound', 'wrapper', 'wrapping', 'wrapt', 'wrasse', 'wrastle', 'wrastled', 'wrath', 'wrathed', 'wrathful', 'wrathfully', 'wrathier', 'wrathiest', 'wrathily', 'wrathing', 'wrathy', 'wreak', 'wreaked', 'wreaker', 'wreaking', 'wreath', 'wreathe', 'wreathed', 'wreathing', 'wreathy', 'wreck', 'wreckage', 'wrecker', 'wreckful', 'wrecking', 'wren', 'wrench', 'wrenched', 'wrenching', 'wrest', 'wrested', 'wrester', 'wresting', 'wrestle', 'wrestled', 'wrestler', 'wrestling', 'wretch', 'wretched', 'wretcheder', 'wried', 'wrier', 'wriest', 'wriggle', 'wriggled', 'wriggler', 'wrigglier', 'wriggliest', 'wriggling', 'wriggly', 'wright', 'wrigley', 'wring', 'wringer', 'wringing', 'wrinkle', 'wrinkled', 'wrinklier', 'wrinkliest', 'wrinkling', 'wrinkly', 'wrist', 'wristband', 'wristdrop', 'wristiest', 'wristlet', 'wristwatch', 'wristy', 'writ', 'writable', 'write', 'writeoff', 'writer', 'writhe', 'writhed', 'writher', 'writhing', 'writing', 'written', 'wrong', 'wrongdoer', 'wrongdoing', 'wronger', 'wrongest', 'wrongful', 'wrongfully', 'wronging', 'wrongly', 'wrote', 'wroth', 'wrothful', 'wrought', 'wrung', 'wry', 'wryer', 'wryest', 'wrying', 'wryly', 'wryneck', 'wurst', 'wurzel', 'wye', 'wyoming', 'wyomingite', 'wyvern', 'xanthate', 'xanthic', 'xanthin', 'xanthine', 'xanthippe', 'xanthochroid', 'xanthoma', 'xanthophyll', 'xebec', 'xenia', 'xenic', 'xenobiology', 'xenocryst', 'xenogamy', 'xenograft', 'xenolith', 'xenolithic', 'xenon', 'xenophobe', 'xenophobia', 'xenophobic', 'xeric', 'xeroderma', 'xerographic', 'xerography', 'xerophthalmia', 'xerophyte', 'xerox', 'xeroxed', 'xeroxing', 'xiphoid', 'xiphosuran', 'xylan', 'xylem', 'xylene', 'xylidine', 'xylitol', 'xylograph', 'xylography', 'xyloid', 'xylophone', 'xylophonist', 'xylose', 'xylotomy', 'xyster', 'yabber', 'yacht', 'yachted', 'yachter', 'yachting', 'yachtman', 'yachtsman', 'yachtsmanship', 'yachtswoman', 'yack', 'yacking', 'yahoo', 'yahooism', 'yahooligan', 'yahooligans', 'yahweh', 'yak', 'yakked', 'yakking', 'yale', 'yam', 'yammer', 'yammerer', 'yammering', 'yamun', 'yang', 'yangtze', 'yank', 'yanked', 'yankee', 'yanking', 'yanqui', 'yap', 'yapper', 'yapping', 'yard', 'yardage', 'yardarm', 'yardbird', 'yarding', 'yardman', 'yardmaster', 'yardstick', 'yare', 'yarely', 'yarer', 'yarest', 'yarmulke', 'yarn', 'yarned', 'yarning', 'yarrow', 'yashmac', 'yashmak', 'yaw', 'yawed', 'yawing', 'yawl', 'yawled', 'yawling', 'yawn', 'yawned', 'yawner', 'yawning', 'yawp', 'yawped', 'yawper', 'yawping', 'yay', 'ycleped', 'yclept', 'ye', 'yea', 'yeah', 'year', 'yearbook', 'yearling', 'yearlong', 'yearly', 'yearn', 'yearned', 'yearner', 'yearning', 'yeast', 'yeasted', 'yeastier', 'yeastiest', 'yeastily', 'yeasting', 'yeasty', 'yegg', 'yeggman', 'yell', 'yelled', 'yeller', 'yelling', 'yellow', 'yellowbellied', 'yellowbelly', 'yellowed', 'yellower', 'yellowest', 'yellowing', 'yellowish', 'yellowknife', 'yellowly', 'yellowy', 'yelp', 'yelped', 'yelper', 'yelping', 'yemenite', 'yen', 'yenned', 'yenning', 'yenta', 'yeoman', 'yeomanly', 'yeomanry', 'yep', 'yerba', 'yeshiva', 'yeshivah', 'yeshivoth', 'yessed', 'yessing', 'yester', 'yesterday', 'yesteryear', 'yet', 'yeti', 'yew', 'yid', 'yield', 'yielder', 'yielding', 'yin', 'yip', 'yipe', 'yippee', 'yippie', 'yipping', 'ymca', 'yod', 'yodel', 'yodeled', 'yodeler', 'yodeling', 'yodelled', 'yodeller', 'yodelling', 'yodle', 'yodled', 'yodler', 'yodling', 'yoga', 'yogee', 'yoghurt', 'yogi', 'yogic', 'yogin', 'yogini', 'yogurt', 'yoke', 'yoked', 'yokel', 'yokelish', 'yokemate', 'yoking', 'yokohama', 'yolk', 'yolked', 'yolkier', 'yolky', 'yon', 'yond', 'yonder', 'yoni', 'yonker', 'yore', 'york', 'yorker', 'yosemite', 'you', 'young', 'younger', 'youngest', 'youngish', 'youngling', 'youngster', 'youngstown', 'younker', 'your', 'yourn', 'yourself', 'youse', 'youth', 'youthen', 'youthened', 'youthening', 'youthful', 'youthfully', 'yow', 'yowed', 'yowie', 'yowing', 'yowl', 'yowled', 'yowler', 'yowling', 'ytterbic', 'ytterbium', 'yttria', 'yttric', 'yttrium', 'yuan', 'yucca', 'yugoslav', 'yugoslavia', 'yugoslavian', 'yuk', 'yukked', 'yukking', 'yukon', 'yule', 'yuletide', 'yummier', 'yummiest', 'yummy', 'yup', 'yuppie', 'yurt', 'ywca', 'zabaione', 'zachariah', 'zag', 'zagging', 'zaire', 'zairian', 'zambezi', 'zambia', 'zambian', 'zanier', 'zaniest', 'zanily', 'zany', 'zanyish', 'zanzibar', 'zap', 'zapping', 'zazen', 'zeal', 'zealand', 'zealander', 'zealot', 'zealotry', 'zealously', 'zebeck', 'zebra', 'zebraic', 'zebrine', 'zebroid', 'zebu', 'zed', 'zee', 'zeitgeist', 'zen', 'zenana', 'zendo', 'zenith', 'zenithal', 'zeolite', 'zephyr', 'zeppelin', 'zero', 'zeroed', 'zeroing', 'zest', 'zested', 'zestful', 'zestfully', 'zestier', 'zestiest', 'zesting', 'zesty', 'zeta', 'zig', 'zigging', 'ziggurat', 'zigzag', 'zigzagging', 'zikurat', 'zilch', 'zillion', 'zillionth', 'zimbabwe', 'zinc', 'zincate', 'zinced', 'zincic', 'zincified', 'zincify', 'zincing', 'zincite', 'zincking', 'zincky', 'zincoid', 'zincy', 'zing', 'zinger', 'zingier', 'zingiest', 'zinging', 'zingy', 'zinkify', 'zinky', 'zinnia', 'zion', 'zionism', 'zionist', 'zip', 'zipper', 'zippering', 'zippier', 'zippiest', 'zipping', 'zippy', 'zircon', 'zirconic', 'zirconium', 'zither', 'zitherist', 'zithern', 'zizzle', 'zizzled', 'zizzling', 'zodiac', 'zodiacal', 'zombie', 'zonal', 'zonation', 'zone', 'zoner', 'zonetime', 'zoning', 'zonked', 'zoo', 'zoologist', 'zoology', 'zoom', 'zoomed', 'zooming', 'zooparasitic', 'zoopathology', 'zoophobia', 'zoophyte', 'zooplankton', 'zori', 'zoroaster', 'zoroastrian', 'zoroastrianism', 'zoster', 'zouave', 'zowie', 'zoysia', 'zucchetto', 'zucchini', 'zulu', 'zuni', 'zurich', 'zwieback', 'zygote', 'zygotic', 'zymase', 'zymogenic', 'zymology', 'zymoplastic', 'zymoscope', 'zymurgy', 'zyzzyva', ];
import numpy as np import matplotlib.pyplot as plt from scipy.sparse import csr_matrix, csc_matrix from exceptions import NotImplementedError, StopIteration class ESN(object): """Methods for training and running an echo state network """ def __init__(self, n_neurons, n_input, n_output): self.n_neurons = n_neurons self.n_input = n_input self.n_output = n_output # recurrent weights self.W = None self.W_shp = (self.n_neurons, self.n_neurons) # input weights self.Q = None self.Q_shp = (self.n_neurons, self.n_input) # output weights self.R = None self.R_shp = (self.n_output, self.n_neurons) # output bias self.b = np.zeros(self.n_output) # decay rate self.r_decay = 1e-5 # learning rate self.l_rate = 1e-3 # network state self.y = np.zeros(self.n_neurons) # input vector self.x = np.zeros(self.n_input) # sparsity of W self.w_sparsity = 10. / n_neurons # number of train iterations in a training epoch self.epoch_length = 10000 self.epochs = 10 # priming iteratoions self.n_prime = 100 #finally, initialize the weights self.init_network() def init_network(self): """Initializes the network parameters """ mask = np.random.randn(*self.W_shp) < self.w_sparsity # enforce sparsity constraint self.W = np.random.randn(*self.W_shp)*mask # scale the weights by 1/(sqrt(# of presynaptic neurons) scale = 1. / np.sqrt(np.sum(mask, axis=1)) self.W = np.dot(self.W.T, np.diag(scale)).T # set input and output weights self.Q = np.random.randn(*self.Q_shp) self.R = np.random.randn(*self.R_shp) # sparsify everything self.W = csr_matrix(self.W) self.Q = csr_matrix(self.Q) #self.R = csr_matrix(self.R) #self.b = csr_matrix(self.b) def step_recurrent(self): """Runs the current dynamics for one timestep """ self.y = np.tanh(self.W.dot(self.y) + self.Q.dot(self.x)) def get_output(self): """Returns the softmaxed output """ z = self.R.dot(self.y) + self.b return np.exp(z) / np.sum(np.exp(z)) def next_input(self): """Sets the next input vector of size (n_input,) to self.x To be implemented by inheriting class """ raise NotImplementedError() def reset_input(self): """Reset the input iterator so that training epochs can be repeated To be implemented by inheriting class """ raise NotImplementedError() def train(self, n_epochs=None): """Trains the network for the specified number of epochs """ n_epochs = n_epochs or self.epochs for e_itr in xrange(n_epochs): print "Training epoch number {}".format(e_itr) self.next_input() for _ in xrange(self.epoch_length): try: # faster to do this inline self.y = np.tanh(self.W.dot(self.y) + self.Q.dot(self.x)) q = self.get_output() self.next_input() # learning rule R_grad = np.outer((self.x - q), self.y)# - self.r_decay * self.R b_grad = self.x - q self.R = self.R + self.l_rate*R_grad self.b = self.b + 10*self.l_rate*b_grad except StopIteration: self.reset_input() def run_network(self, itr, prime=True): """Primes the network with n_prime input and then runs it for itr iterations. For every iteration appends the softmaxed output to a list and returns the list """ output = [] state = [] assert self.n_output == self.n_input if prime: self.prime_network(500) for _ in xrange(itr): q = self.get_output() # breaks abstraction # chooses state according to the probability distribution new_state = np.where(np.cumsum(q) > np.random.random())[0][0] self.x = np.zeros(self.n_input) self.x[new_state] = 1 self.step_recurrent() state.append(self.y) output.append(self.x) return output, np.array(state) def prime_network(self, itr): """Primes the network with itr inputs """ self.reset_input() for _ in xrange(self.n_prime): self.next_input() self.step_recurrent() def plot_state(self, itr, n_nodes=5): """Selects n_nodes at random from the network state and plots their time series activations """ plt.ion() nodes = np.random.randint(self.n_neurons, size=n_nodes) output, state = self.run_network(itr, False) activations = state[:, nodes] t = np.linspace(1, itr, num=itr) for y_series in activations.T: plt.plot(t, y_series) plt.title("Activation of {} randomly chosen nodes over time".format(n_nodes)) plt.xlabel("Iteration") plt.ylabel("Activation") class CharESN(ESN): """ESN that implements character prediction """ def __init__(self, n_neurons, path): self.path = path self.char_to_int = {} self.text = "" self.read_in() self.int_to_char = {i : char for (char, i) in self.char_to_int.iteritems()} self.n_alphabet = len(self.char_to_int) ESN.__init__(self, n_neurons, self.n_alphabet, self.n_alphabet) self.reset_input() def read_in(self): """Reads in the text while building the char dictionary """ with open(self.path, 'r') as lewis: for line in lewis: for char in line.lower(): self.text += char if char not in self.char_to_int: curr_len = len(self.char_to_int) self.char_to_int[char] = curr_len def vec_gen(self): """Generator for input. next returns the vector corresponding to the next character in the text """ for char in self.text: vec = np.zeros(self.n_input) vec[self.char_to_int[char]] = 1 yield vec def reset_input(self): """Resets the char vector generator """ self.input_generator = self.vec_gen() def next_input(self): self.x = self.input_generator.next() def generate_text(self, n_chars, prime=True): """Primes the network and then generates n_chars of text """ outputs, state = self.run_network(n_chars, prime) gen_text = "" for out in outputs: gen_text += self.int_to_char[np.argmax(out)] print gen_text class WordESN(ESN): """ESN that implements word prediction """ def __init__(self, n_neurons, path): self.path = path self.word_to_int = {} self.text = [] self.read_in() self.int_to_word = {i : word for (word, i) in self.word_to_int.iteritems()} self.n_alphabet = len(self.word_to_int) ESN.__init__(self, n_neurons, self.n_alphabet, self.n_alphabet) self.reset_input() def read_in(self): """Reads in the text while building the char dictionary """ with open(self.path, 'r') as lewis: for line in lewis: line_list = [] for word in self.parse_line(line): line_list.append(word) if word not in self.word_to_int: curr_len = len(self.word_to_int) self.word_to_int[word] = curr_len self.text.append(line_list) def parse_line(self, line): """Splits a line into its atomic components """ split_on_space = line.lower().split() parsed = [] for tok in split_on_space: first_alpha = 0 last_alpha = len(tok) + 1 for ind in xrange(len(tok)): if tok[ind].isalpha(): first_alpha = ind break for ind in reversed(xrange(len(tok))): if tok[ind].isalpha(): last_alpha = ind break tok_split = [tok[0:first_alpha], tok[first_alpha:last_alpha+1], tok[last_alpha+1:len(tok) + 1] ] for subtok in tok_split: if len(subtok) > 0: parsed.append(subtok) parsed.append("\n") return parsed def vec_gen(self): """Generator for input. next returns the vector corresponding to the next character in the text """ for line in self.text: for word in line: vec = np.zeros(self.n_input) vec[self.word_to_int[word]] = 1 yield vec def reset_input(self): """Resets the char vector generator """ self.input_generator = self.vec_gen() def next_input(self): self.x = self.input_generator.next() def generate_text(self, n_chars, prime=True): """Primes the network and then generates n_chars of text """ outputs, state = self.run_network(n_chars, prime) gen_text = "" for out in outputs: gen_text += self.int_to_word[np.argmax(out)] gen_text += " " print gen_text alice = CharESN(750, 'alice_in_wonderland.txt') lewis = WordESN(500, 'alice_in_wonderland.txt')
from scipy.spatial import distance from imutils import face_utils import imutils import dlib import cv2 import multiprocessing as mp # Define the function for calculating eye aspect ratio with eucilidean distance def eye_aspect_ratio(eye): # Calculate the distance between the 3 pairs of point. A = distance.euclidean(eye[1], eye[5]) B = distance.euclidean(eye[2], eye[4]) C = distance.euclidean(eye[0], eye[3]) # Find the EAR and return to function caller return ((A + B) / (2.0 * C)) # Get a function from dlib to be used to detect faces, or 'subjects' detect = dlib.get_frontal_face_detector() # Get the .dat file that stores the prediction model used by dlib, and pass it into the function to get a model out. # Use relative location for the shape predictor file instead of abs. path for consistency across different filesystems. # Should I use the os.path to get current PWD in order to get the address or just do relative import? predict_data = dlib.shape_predictor('./shape_predictor_68_face_landmarks.dat') # Give names to the detected features, 'eyes' (lStart, lEnd) = face_utils.FACIAL_LANDMARKS_68_IDXS["left_eye"] (rStart, rEnd) = face_utils.FACIAL_LANDMARKS_68_IDXS["right_eye"] # Function to run in Daemonic child process that just reads the face and puts data from I/O into the Queues def get_face(face_queue, gray_queue, frame_queue): # Open the camera by Index 0 for the default camera connected and create a video stream capture object from that. cap = cv2.VideoCapture(0) # Loop/wait until the video capture is opened # while not cv2.VideoCapture.isOpened(): # print('Waiting for camera to open') while True: # Read and store the newly captured image ret, frame = cap.read() # Get the grayscale image out from the original image to make it easier for processing gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Detect faces, after applying the filter for color recognition subjects = detect(gray, 0) try: # Try to get the first/closest face out subject = subjects[0] # if face detected, meaning that index 0 holds a valid value, then put face into the Queue face_queue.put(subject) # Put the grayscale image and the frame into their Respective Queues too gray_queue.put(gray) frame_queue.put(frame) except: # If index 0 is null, indexError will be raised, meaning no face detected. # Even if there is no face detected, put frame into Queue to continue displaying video feed frame_queue.put(frame) # Function to run in child process to predict Drowsiness based on the images/faces read from the Queues def prediction_func(face_queue, gray_queue, frame_queue): quit_flag = False thresh = 0.22 # Threshold value for the Eye Aspect Ratio. count = 0 # Variable used to keep track of the consecutive number of times the 'EAR' is below threshold # Infinite loop to read frames from the video output and put into a queue # Loop till user press q to set the quit_flag in order to quit the program while not quit_flag: # Read the pipe, do the below only if image avail in the pipe while not face_queue.empty(): # Get the last face in the face queue face = face_queue.get() while face_queue.qsize(): face = face_queue.get() gray = gray_queue.get() while gray_queue.qsize(): gray = gray_queue.get() shape = predict_data(gray, face) # Convert the shape data to a NumPy Array shape = face_utils.shape_to_np(shape) leftEye = shape[lStart:lEnd] rightEye = shape[rStart:rEnd] ear = (eye_aspect_ratio(leftEye) + eye_aspect_ratio(rightEye)) / 2.0 # Draw on the frame so that the user can see the eye tracking in real time. # Create the contours out before drawing them. leftEyeHull = cv2.convexHull(leftEye) rightEyeHull = cv2.convexHull(rightEye) frame = frame_queue.get() while frame_queue.qsize(): frame = frame_queue.get() # To draw out the contours around the eyes cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1) cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1) print(f"EAR: {ear}") if ear < thresh: count += 1 # Number of frames where EAR is below threshold before counted as falling asleep if count >= 3: # Alert the user by putting text onto the frame directly. cv2.putText(frame, "**********************ALERT!**********************", (20, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) cv2.putText(frame, "**********************ALERT!**********************", (20, 460), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) # Alert the user by sounding the alarm. # Pass the event via the data # alarm() else: count = 0 # Reset the count # Read the frame from the Queue to display cv2.imshow("Frame", frame) if (cv2.waitKey(1) & 0xFF) == ord("q"): # Set a quit flag quit_flag = True break # Even if there is no face detected, the frame should still be read and displayed cv2.imshow("Frame", frame_queue.get()) if (cv2.waitKey(1) & 0xFF) == ord("q"): # Break the loop if user pressed 'q' when no subjects are detected break def main(): # Use the 'spawn' method to start a new Process mp.set_start_method('spawn') # Create the Queue objects for the different data face_queue = mp.Queue() gray_queue = mp.Queue() frame_queue = mp.Queue() # Spawn a new Daemonic Process to get the face of the user, Daemonic to allow auto exit/close upon main Process ending. get_face_p = mp.Process(target=get_face, args=(face_queue, gray_queue, frame_queue,), daemon=True) # Start the Process immediately after creation get_face_p.start() # Spawn a new Process to do the prediction and detection of the User's eyes prediction_func_p = mp.Process(target=prediction_func, args=(face_queue, gray_queue, frame_queue,)) # Start the Process immediately after creation prediction_func_p.start() # The signal handler should not be used. import signal def signal_handler(signal, frame): print("Program interrupted!") # Close the camera and the display window cv2.destroyAllWindows() # Close and destroy all the Queues face_queue.close() gray_queue.close() frame_queue.close() # Below no longer needed as the Process is daemonic # Kill and Stop the get_face process with a SIGTERM signal # get_face_p.terminate() exit(0) # Pass in the signal_handler to run when the INTerrupt signal is received # signal.signal(signal.SIGINT, signal_handler) # Wait for the predicition process to end on Key Press prediction_func_p.join() # Destroy all the HighGUI windowws cv2.destroyAllWindows() # Close all the Queues to release the resources and prevent program corruption face_queue.close() gray_queue.close() frame_queue.close() # No longer need to kill process like below since the get_face_p Process is now ran as a Daemonic Process # Always close all the Queues before terminating the Processes to preven the Queues from corrupting # Kill and Stop the get_face process with a SIGTERM signal # get_face_p.terminate() # get_face_p.join() # get_face_p.close() if __name__ == "__main__": # Run the main function if this module used as program entry point main()
""" Blackjack.py - <NAME> - Spring 2014 Implementation of Blackjack. Enjoy: http://www.codeskulptor.org/#user31_R8PVRLqskziSghE.py Although we used class-specific CodeSculptor for graphics, most of the methods and the rest of the concepts are similar if not the same in other Python librararies, such as Pygame. Learned to: - play Blackjack - nice intro to OOP - track/flowchart complex logic """ import simplegui import random # load card sprite - 950x392 - source: jfitz.com CARD_SIZE = (73, 98) CARD_CENTER = (36.5, 49) card_images = simplegui.load_image("http://commondatastorage.googleapis.com/codeskulptor-assets/cards.jfitz.png") CARD_BACK_SIZE = (71, 96) CARD_BACK_CENTER = (35.5, 48) card_back = simplegui.load_image("http://commondatastorage.googleapis.com/codeskulptor-assets/card_back.png") # initialize global variables all_cards = [] in_play = False win_loose_mess = "" hit_stand_mess = "Hit or Stand?" score = 0 player_hand = [] dealer_hand = [] # define globals for cards SUITS = ['C', 'S', 'H', 'D'] RANKS = ['A', '2', '3', '4', '5', '6', '7', '8', '9', 'T', 'J', 'Q', 'K'] VALUES = {'A':1, '2':2, '3':3, '4':4, '5':5, '6':6, '7':7, '8':8, '9':9, 'T':10, 'J':10, 'Q':10, 'K':10} # define card class class Card: def __init__(self, suit, rank): if (suit in SUITS) and (rank in RANKS): self.suit = suit self.rank = rank else: self.suit = None self.rank = None print "Invalid card: ", self.suit, self.rank def __str__(self): return self.suit + self.rank def get_suit(self): return self.suit def get_rank(self): return self.rank def draw(self, canvas, pos): card_loc = (CARD_SIZE[0] * (0.5 + RANKS.index(self.rank)), CARD_SIZE[1] * (0.5 + SUITS.index(self.suit))) canvas.draw_image(card_images, card_loc, CARD_SIZE, [pos[0] + CARD_SIZE[0] / 2, pos[1] + CARD_SIZE[1] / 2], CARD_SIZE) # define hand class class Hand: def __init__(self, deck_cards, player_hand, dealer_hand): self.deck_cards = deck_cards self.player_hand = player_hand self.dealer_hand = dealer_hand def __str__(self): pass # replace with your code def add_card(self): card = self.deck_cards[4] self.player_hand += [card] return self.player_hand def add_card_dealer(self): card = self.deck_cards[4] self.dealer_hand += [card] return self.dealer_hand # count aces as 1, if the hand has an ace, then add 10 to hand value if don't bust def get_value(self, player_hand): final_value = [] self.player_hand = player_hand for card in self.player_hand: final_value.append(VALUES[card[1]]) if sum(final_value) > 21: final_value = [100,1] return sum(final_value) def get_value_dealer(self, dealer_hand): final_value = [] self.dealer_hand = dealer_hand for card in self.dealer_hand: final_value.append(VALUES[card[1]]) if sum(final_value) > 21: final_value = [100,1] return sum(final_value) def busted(self): in_play = False pass # replace with your code def draw(self, canvas, p): pass # replace with your code # define deck class class Deck: def __init__(self): self.RANKS = RANKS self.SUITS = SUITS self.deck_cards = [] # add cards back to deck and shuffle def shuffle_cards(self): for rank in self.RANKS: card_c = [self.SUITS[0], rank] card_h = [self.SUITS[1], rank] card_d = [self.SUITS[2], rank] card_s = [self.SUITS[3], rank] self.deck_cards.append(card_c) self.deck_cards.append(card_h) self.deck_cards.append(card_d) self.deck_cards.append(card_s) random.shuffle(self.deck_cards) return self.deck_cards def deal_card(self): card = self.deck_cards[4] self.deck_cards.pop(4) return card #define callbacks for buttons def deal(): global win_loose_mess, hit_stand_mess, in_play, score, deck, all_cards global dealers_card_1, dealers_card_2, players_card_1, players_card_2, player_hand, dealer_hand if in_play: player_hand = [] dealer_hand = [] all_cards = [] deck = Deck() # initiate the game all_cards = deck.shuffle_cards() #Initiate the cards dealers_card_1 = Card(all_cards[0][0], all_cards[0][1]) dealers_card_2 = Card(all_cards[1][0], all_cards[1][1]) dealer_hand = [[dealers_card_1.get_suit(), dealers_card_1.get_rank()], [dealers_card_2.get_suit(), dealers_card_2.get_rank()]] players_card_1 = Card(all_cards[2][0], all_cards[2][1]) players_card_2 = Card(all_cards[3][0], all_cards[3][1]) player_hand = [[players_card_1.get_suit(),players_card_1.get_rank()], [players_card_2.get_suit(), players_card_2.get_rank()]] score = score win_loose_mess = "" hit_stand_mess = "Hit or Stand?" else: # if the game has not been played yet deck = Deck() # initiate the game all_cards = deck.shuffle_cards() in_play = True def hit(): global player_hand, dealer_hand, player_final_value, score, win_loose_mess, hit_stand_mess, in_play # if the hand is in play, hit the player if in_play: playing_hand = Hand(all_cards, player_hand, dealer_hand) player_hand = playing_hand.add_card() player_hand_value = playing_hand.get_value(player_hand) player_final_value = player_hand_value print player_hand_value #print player_hand_value if player_hand_value == 101: score -= 1 win_loose_mess = "You Lost - over 21" hit_stand_mess = "New Deal?" # if busted, assign an message to outcome, update in_play and score def stand(): global player_hand, dealer_hand, player_final_value, dealer_final_value, score, win_loose_mess, hit_stand_mess, in_play dealing_hand = Hand(all_cards, player_hand, dealer_hand) dealer_hand_value = dealing_hand.get_value_dealer(dealer_hand) playing_hand = Hand(all_cards, player_hand, dealer_hand) player_final_value = playing_hand.get_value(player_hand) if dealer_hand_value == 101: score += 1 win_loose_mess = "You Win!" hit_stand_mess = "New Deal?" elif dealer_hand_value < 17: dealer_hand = dealing_hand.add_card_dealer() dealer_hand_value = dealing_hand.get_value_dealer(dealer_hand) if dealer_hand_value == 101: score += 1 win_loose_mess = "You Win!" hit_stand_mess = "New Deal?" elif dealer_hand_value > player_final_value: score -= 1 win_loose_mess = "You Lost" hit_stand_mess = "New Deal?" elif dealer_hand_value < player_final_value: score += 1 win_loose_mess = "You Win!" hit_stand_mess = "New Deal?" elif dealer_hand_value == player_final_value: score -= 1 win_loose_mess = "You Lost" hit_stand_mess = "New Deal?" elif dealer_hand_value > 17: if dealer_hand_value > player_final_value: score -= 1 win_loose_mess = "You Lost" hit_stand_mess = "New Deal?" elif dealer_hand_value < player_final_value: score += 1 win_loose_mess = "You Win!" hit_stand_mess = "New Deal?" elif dealer_hand_value == player_final_value: score -= 1 win_loose_mess = "You Lost" hit_stand_mess = "New Deal?" def draw(canvas): global all_cards, win_loose_mess, hit_stand_mess, score, player_hand, dealer_hand # draw static text: blackjack, dealer, player canvas.draw_text('Blackjack', (5, 50), 50, 'Blue', 'serif') canvas.draw_text('Dealer', (20, 150), 40, 'Black', 'serif') canvas.draw_text('Player', (20, 350), 40, 'Black', 'serif') # draw dynamic text: Score, "Hit or Stand" or "New Deal" canvas.draw_text('Score: '+str(score), (400, 50), 40, 'Black', 'serif') canvas.draw_text(hit_stand_mess, (230, 350), 40, 'Black', 'serif') canvas.draw_text(win_loose_mess, (230, 150), 40, 'Black', 'serif') dealers_card_1.draw(canvas, [60, 170]) dealers_card_2.draw(canvas, [170, 170]) players_card_1.draw(canvas, [60, 400]) players_card_2.draw(canvas, [170, 400]) if len(dealer_hand) == 3: dealers_card_3 = Card(all_cards[4][0], all_cards[4][1]) dealers_card_3.draw(canvas, [290, 170]) dealer_hand = [[dealers_card_1.get_suit(),dealers_card_1.get_rank()], [dealers_card_2.get_suit(), dealers_card_2.get_rank()], [dealers_card_3.get_suit(), dealers_card_3.get_rank()]] if len(player_hand) == 3: players_card_3 = Card(all_cards[5][0], all_cards[5][1]) players_card_3.draw(canvas, [290, 400]) player_hand = [[players_card_1.get_suit(),players_card_1.get_rank()], [players_card_2.get_suit(), players_card_2.get_rank()], [players_card_3.get_suit(), players_card_3.get_rank()]] if len(dealer_hand) == 4: dealers_card_3 = Card(all_cards[4][0], all_cards[4][1]) dealers_card_3.draw(canvas, [290, 170]) dealers_card_4 = Card(all_cards[6][0], all_cards[6][1]) dealers_card_4.draw(canvas, [400, 170]) dealer_hand = [[dealers_card_1.get_suit(),dealers_card_1.get_rank()], [dealers_card_2.get_suit(), dealers_card_2.get_rank()], [dealers_card_3.get_suit(), dealers_card_3.get_rank()], [dealers_card_4.get_suit(), dealers_card_4.get_rank()]] if len(player_hand) == 4: players_card_3 = Card(all_cards[5][0], all_cards[5][1]) players_card_3.draw(canvas, [290, 400]) players_card_4 = Card(all_cards[7][0], all_cards[7][1]) players_card_4.draw(canvas, [400, 400]) player_hand = [[players_card_1.get_suit(),players_card_1.get_rank()], [players_card_2.get_suit(), players_card_2.get_rank()], [players_card_3.get_suit(), players_card_3.get_rank()], [players_card_4.get_suit(), players_card_4.get_rank()]] # initialization frame frame = simplegui.create_frame("Blackjack", 600, 600) frame.set_canvas_background("Green") #create buttons and canvas callback frame.add_button("Deal", deal, 200) frame.add_button("Hit", hit, 200) frame.add_button("Stand", stand, 200) frame.set_draw_handler(draw) # deal an initial hand deal() #Initiate the cards dealers_card_1 = Card(all_cards[0][0], all_cards[0][1]) dealers_card_2 = Card(all_cards[1][0], all_cards[1][1]) dealer_hand = [[dealers_card_1.get_suit(), dealers_card_1.get_rank()], [dealers_card_2.get_suit(), dealers_card_2.get_rank()]] players_card_1 = Card(all_cards[2][0], all_cards[2][1]) players_card_2 = Card(all_cards[3][0], all_cards[3][1]) player_hand = [[players_card_1.get_suit(),players_card_1.get_rank()], [players_card_2.get_suit(), players_card_2.get_rank()]] # get things rolling frame.start()
""" A simple pytest plugin to test schemas against valid and invalid test data. When this plugin is activated, subclasses of BaseDatatypeTest define tests for a JSON schema. """ import contextlib import importlib import json import re from dataclasses import dataclass from pathlib import Path from typing import Callable, List from urllib.parse import quote, unquote import json5 import jsonpatch import pyjq import pytest from jsonpointer import JsonPointer from jsonschema import Draft7Validator, RefResolver, ValidationError from rfc3987 import parse, resolve, compose def uri_to_path(uri): parts = parse(uri, 'URI') if parts['scheme'] != 'file': raise ValueError(f'uri is not a file:// URL: {uri}') return Path(unquote(parts['path'])) def path_to_uri(path: Path): if isinstance(path, str): path = Path(path) uri = path.as_uri() if path.is_dir(): # paths which are DIRs should end with a slash so that we can resolve # off them without losing the dir. return uri + '/' return uri def validate_path_under_dir(dir): def path_under_dir_validator(path): if dir not in path.parents: raise ValueError(f'{path} is not a sub-path of {dir}') return path_under_dir_validator def url_remapper(src, dest): src_parts = parse(src, 'URI') dest_parts = parse(dest, 'URI') src_path = Path(unquote(src_parts['path'])).resolve() dest_path = Path(unquote(dest_parts['path'])).resolve() def remap(url): url_parts = parse(url, 'URI') if not (url_parts['scheme'] == src_parts['scheme'] and url_parts['authority'] == src_parts['authority']): return False, url url_path = Path(unquote(url_parts['path'])).resolve() if src_path != url_path and src_path not in url_path.parents: return False, url result_path = dest_path / url_path.relative_to(src_path) # Use a trailing slash if the incoming path had one. This facilitates # further URI resolution operations. if url_parts['path'].endswith('/'): final_path = f'{result_path}/' else: final_path = str(result_path) return True, (compose( scheme=dest_parts['scheme'], authority=dest_parts['authority'], path=quote(final_path), query=url_parts['query'], fragment=url_parts['fragment'])) return remap def handle_file_uri_request(uri, validate_path=None): path = uri_to_path(uri) if validate_path is not None: validate_path(path) with open(path, 'r') as f: return json.load(f) def schema_url_handler(schema_id_base_url, local_dir): """Handle requests for schema ID URLs by providing local schema files """ remap = url_remapper(schema_id_base_url, path_to_uri(local_dir)) validator = validate_path_under_dir(local_dir) def handle(uri): remapped, uri = remap(uri) if not remapped: raise ValueError(f'Unable to handle URI: {uri}') return handle_file_uri_request(uri, validate_path=validator) return handle @dataclass class PatchedJsonValue: base_value: dict patch: list @property def value(self): return jsonpatch.apply_patch(self.base_value, self.patch) @classmethod def from_testcase(cls, test_case_path, base_path_validator=None): with open(test_case_path) as f: test_case = json5.load(f) tc_path_url = path_to_uri(test_case_path) base_value_path = uri_to_path(resolve(tc_path_url, test_case['base'])) if base_path_validator is not None: base_path_validator(base_value_path) with open(base_value_path) as f: base_value = json5.load(f) return PatchedJsonValue(base_value=base_value, patch=test_case['patch']) @dataclass class InvalidInstanceTestCase: data: PatchedJsonValue validation_error_validator: Callable[[List[ValidationError]], None] def create_messages_validator(expected_error): if isinstance(expected_error, str): expected_error = {'contains': expected_error} [(method, value)] = expected_error.items() if method == 'exact': msg = f'{value!r} to equal' pattern = re.compile(f'^{re.escape(value)}$') elif method == 'contains': msg = f'the substring {value!r} to occur in' pattern = re.compile(re.escape(value)) elif method == 'regex': msg = f'regex search for {value} to match' pattern = re.compile(value) else: raise ValueError( f'Unsupported expectedError value: {expected_error!r}') def validate(error_messages): nonlocal msg if not any(pattern.search(msg) for msg in error_messages): pytest.fail(f'\ Expected {msg} at least one of {error_messages}', pytrace=False) return validate def create_validation_error_validator(expected_errors): if isinstance(expected_errors, str): expected_errors = [expected_errors] validators = [create_messages_validator(e) for e in expected_errors] def validate_validation_errors(errors): if len(errors) == 0: pytest.fail('instance unexpectedly passed schema validation') messages = [e.message for e in errors] for validator in validators: validator(messages) return validate_validation_errors @pytest.fixture(scope='class') def valid_instance(valid_instance_path): with open(valid_instance_path) as f: return json5.load(f) @pytest.fixture(scope='class') def invalid_instance_testcase(invalid_testcase_path, test_data_dir): with open(invalid_testcase_path) as f: test_case = json5.load(f) tc_path_url = path_to_uri(invalid_testcase_path) base_value_path = uri_to_path(resolve(tc_path_url, test_case['base'])) validate_path_under_dir(test_data_dir)(base_value_path) with open(base_value_path) as f: base_value = json5.load(f) patched_value = PatchedJsonValue( base_value=base_value, patch=test_case['patch']) err_validator = create_validation_error_validator( test_case.get('expectedErrors') or ()) return InvalidInstanceTestCase( data=patched_value, validation_error_validator=err_validator) @pytest.fixture(scope='class') def ref_resolver(schema_base_uri, schema_dir): base_scheme = parse(schema_base_uri, 'URI')['scheme'] handlers = { base_scheme: schema_url_handler(schema_base_uri, schema_dir) } return RefResolver(base_uri=path_to_uri(schema_dir), referrer=None, handlers=handlers) @pytest.fixture(scope='class', ids=lambda s: s.get('$id', '<schema-without-id>')) def schema(ref_resolver, schema_json): Draft7Validator.check_schema(schema_json) return Draft7Validator(schema_json, resolver=ref_resolver) @pytest.fixture(scope='class') def schema_json(ref_resolver, schema_id): _, schema = ref_resolver.resolve(schema_id) return schema def pytest_generate_tests(metafunc): if metafunc.cls and issubclass(metafunc.cls, BaseDatatypeTest): metafunc.cls.generate_parameters(metafunc) def get_file(cls): return importlib.import_module(cls.__module__).__file__ class BaseDatatypeTest: """ When subclassed, this tests a schema against a set of valid and invalid test cases, defined as JSON files in the following directory structure:: schemas ├── collection.json ├── common.json ├── cudl.item.schema.json ├── item.schema.json └── mudl.item.schema.json tests ├── collection │   ├── invalid │   │   ├── additional-root-properties.json5 │   │   └── missing-type.json5 │   └── valid │   ├── kitchen-sink.json │   └── minimal.json └── test_schemas.py The schema's URI is mapped to the ``schemas`` directory. Subclasses must define the class properties: - ``data_type`` — The name of the schema being tested - ``schema_base_uri`` — The URI to map to the local schema dir. Typically this is the URI of the schema being tested without the final path component. For example, ``https://schemas.cudl.lib.cam.ac.uk/package/v1/`` And optionally: - ``expected_valid_count`` - ``expected_invalid_count`` - ``schema_dir`` — The path to the directory containing the schema files. defaults to ``{dir_of_test_module}/../schemas``. If the number of valid/invalid test cases is less than these values the test setup will fail. If it's more then a warning will be triggered. Testcase Data ============= """ # These must be overridden data_type = None schema_base_uri = None # These have defaults if not specified expected_valid_count = None expected_invalid_count = None schema_dir = None @classmethod def get_schema_base_uri(cls): if cls.schema_base_uri is None: raise TypeError(f'No schema_base_uri specified, {cls} must have a ' f'schema_base_uri property') return cls.schema_base_uri @classmethod def get_schema_dir(cls): if cls.schema_dir: return cls.schema_dir return (Path(get_file(cls)).parent / '../schemas').resolve() @classmethod def get_data_type(cls): if cls.data_type is None: raise TypeError(f'No data_type specified, {cls} must have a ' f'data_type property') return cls.data_type @classmethod def get_schema_id(cls): return f'{cls.get_data_type()}.json' @classmethod def get_test_data_dir(cls): return Path(get_file(cls)).parent / cls.get_data_type() @classmethod def list_testcases(cls, testcase_type): dir = cls.get_test_data_dir() return (set(dir.glob(f'{testcase_type}/*.json')) | set(dir.glob(f'{testcase_type}/*.json5'))) @classmethod def validate_testcase_paths(cls, testcase_type, expected_count, paths): if expected_count is not None and len(paths) != expected_count: msg = ( f'expected {cls.get_data_type()} to have {expected_count} {testcase_type} ' f'testcases but found {len(paths)}') assert len(paths) == expected_count, msg @classmethod def generate_parameters(cls, metafunc): data_type = cls.get_data_type() valid_count = cls.expected_valid_count invalid_count = cls.expected_invalid_count if 'data_type' in metafunc.fixturenames: metafunc.parametrize('data_type', [data_type], scope='class') if 'schema_id' in metafunc.fixturenames: metafunc.parametrize('schema_id', [f'{data_type}.json'], scope='class') if 'schema_base_uri' in metafunc.fixturenames: metafunc.parametrize( 'schema_base_uri', [cls.get_schema_base_uri()], scope='class') if 'schema_dir' in metafunc.fixturenames: metafunc.parametrize( 'schema_dir', [cls.get_schema_dir()], scope='class', ids=lambda x: str(x)) if 'test_data_dir' in metafunc.fixturenames: metafunc.parametrize( 'test_data_dir', [cls.get_test_data_dir()], scope='class', ids=lambda x: str(x)) if 'valid_instance_path' in metafunc.fixturenames: paths = cls.list_testcases('valid') cls.validate_testcase_paths('valid', valid_count, paths) metafunc.parametrize('valid_instance_path', paths, scope='class', ids=lambda x: str(x)) if 'invalid_testcase_path' in metafunc.fixturenames: paths = cls.list_testcases('invalid') cls.validate_testcase_paths('invalid', invalid_count, paths) metafunc.parametrize( 'invalid_testcase_path', paths, scope='class', ids=lambda x: str(x)) def test_schema_matches_valid_instance(self, schema, valid_instance): with describe_validation_error(): schema.validate(valid_instance) def test_schema_rejects_invalid_instance( self, schema, invalid_instance_testcase: InvalidInstanceTestCase): errors = list(schema.iter_errors( invalid_instance_testcase.data.value)) invalid_instance_testcase.validation_error_validator(errors) @contextlib.contextmanager def describe_validation_error(instance_name=None, schema_name=None, pytrace=False): try: yield except ValidationError as e: msg = format_validation_error(e, instance_name=instance_name, schema_name=schema_name) pytest.fail(msg, pytrace=pytrace) def format_validation_error(err: ValidationError, instance_name=None, schema_name=None): instance_path = JsonPointer.from_parts(err.absolute_path).path schema_path = JsonPointer.from_parts(err.absolute_schema_path).path return f'''\ {instance_name or 'instance'} violates \ {f'schema {schema_name}' if schema_name else 'schema'} at path: {instance_path} description: {err.message} violated schema rule: {schema_path}''' class RequireExplicitAdditionalProperties: def test_schema_uses_no_implicit_additional_properties(self, schema_id, schema_json): implicit_paths = pyjq.all('''\ path(..|select((.|type) == "object" and (.type? == "object") and (has("additionalProperties")|not))) |map(tostring)|join("/")|"/\\(.)"''', schema_json) assert implicit_paths == [], ( f'Schema {schema_id!r} must explicitly state whether its object schemas accept' f' additional properties. It contains object schemas that do not at the ' f'following JSON paths: {implicit_paths}') class RequireExplicitOptionalProperties: def test_schema_uses_no_implicit_empty_required_property_lists(self, schema_id, schema_json): implicit_paths = pyjq.all('''\ path(..|select((.|type) == "object" and (.type? == "object") and has("properties") and (has("required")|not))) |map(tostring)|join("/")|"/\\(.)"''', schema_json) assert implicit_paths == [], ( f'Schema {schema_id!r} must explicitly use "required": [] when an object ' f'schema specifies named properties but has no required properties. It ' f'contains object schemas that do not at the following JSON paths: ' f'{implicit_paths}')
<reponame>tblondelle/TransferLearningProject # -*- coding: utf-8 -*- from __future__ import unicode_literals, print_function, division from io import open import unicodedata import string import re import random import os import time from sklearn.feature_extraction.text import CountVectorizer from sklearn.decomposition import TruncatedSVD from sklearn.feature_extraction.text import TfidfVectorizer from numpy.random import permutation import torch import torch.nn as nn from torch.autograd import Variable from torch import optim import torch.nn.functional as F import time import math import matplotlib.pyplot as plt use_cuda = torch.cuda.is_available() print("Utilisation de la carte graphique :",use_cuda) def asMinutes(s): m = math.floor(s / 60) s -= m * 60 return '%dm %ds' % (m, s) def timeSince(since, percent): now = time.time() s = now - since es = s / (percent) rs = es - s return '%s (- %s)' % (asMinutes(s), asMinutes(rs)) class my_MLP(nn.Module): def __init__(self, input_size, hidden_size,batch_size, n_layers=1): super(my_MLP, self).__init__() self.n_layers = n_layers self.hidden_size = hidden_size self.batch_size = batch_size self.input_size = input_size self.linear1 = nn.Linear(input_size,hidden_size) self.linear2 = nn.Linear(hidden_size, hidden_size) self.linear3 = nn.Linear(hidden_size, 1) # le réseaux linéaires sert à ce que la sortie ait la bonne taille def forward(self, input): # Entrées : # input (variable(mat)) : les instances # Sortie # Variable(vect) : les prédictions X_int_1 = F.relu(self.linear1(input)) X_int_2 = F.relu(self.linear2(X_int_1)) return torch.tanh(self.linear3(X_int_2)) def train_once(self, input_variable, target_variable, optimizer, criterion): # Réalise l'entraînement pour une seule epoch # Entrées : # - n_epochs (int) : nombre de fois qu'on applique toutes les instance de l'ensemble d'apprentissage # - input_variable Variable(mat) : instances d'apprentissage # - target_variable Variable(vect(+1|-1))) : labels # - optimizer (pytorch object) : le résultat de optim.SGD ou optim.Adam # - criterion (pytorch object) : le résultat de nn.L1Loss ou nn.MSELoss # Sorties : # none optimizer.zero_grad() input_length = input_variable.size()[0] output= self(input_variable) loss = criterion(output.view(1,-1), target_variable.view(-1)) loss.backward() optimizer.step() return loss.data[0] def trainIters(self, n_epochs, training_pairs, te_pairs, learning_rate, print_every=1000, eval_every = 1000): # Réalise l'entraînement complet, à partir des ensembles d'apprentissage # Entrées : # - n_epochs (int) : nombre de fois qu'on applique toutes les instance de l'ensemble d'apprentissage # - training_pairs (Variable(mat), Variable(vect(+1|-1))) ) : instances d'apprentissage # - te_pairs (list of (Variable(vect), Variable(+1|-1))) : instances de test # - learning_rate (float) : devine ;) # - print_every (int) : imprime l'erreur moyenne toutes les print_every epochs # - eval_every (int) : teste le NN sur la base de test et imprime la matrice de confusion # Sorties : # none start = time.time() plot_losses = [] print_loss_total = 0 # Reset every print_every #optimizer = optim.SGD(self.parameters(), lr=learning_rate) # Autre choix possible : optimizer = optim.Adam(self.parameters(), lr=learning_rate) criterion = nn.L1Loss() #criterion = nn.MSELoss() for epoch in range(1, n_epochs + 1): input_variable = training_pairs[0] target_variable = training_pairs[1] loss = self.train_once(input_variable, target_variable, optimizer, criterion) print_loss_total += loss if epoch % print_every == 0: print_loss_avg = print_loss_total / print_every print_loss_total = 0 print('%s (%d %d%%) %.4f' % (timeSince(start, epoch / n_epochs), epoch, epoch / n_epochs * 100, print_loss_avg)) if epoch % eval_every == 0: self.evaluateRandomly(te_pairs) # show global results def evaluateRandomly(self, pairs): # evaluate on all pairs, print the confusion matrix n_successes = 0 n_pos = 0 # also computes the proportion of positive reviews TP,TN,FP,FN = 0,0,0,0 for pair in pairs: # replace with pairs[:n] for testing output = self(pair[1]) #success = (output[int(pair[1])] == max(output)) note = pair[0].data[0,0] predicted = output.data[0] success = (note*predicted > 0) if success : n_successes += 1 if note>0: TP += 1 else: TN += 1 else: if note>0: FP += 1 else: FN += 1 n_pos = n_pos+1 if note==1 else n_pos print('') print('') print('Confusion matrix ') print() print(" \t\t Actual class") print(" \t\t Pos \t Neg") print("Predicted Pos \t {} \t {}".format(TP,FN)) print(" Neg \t {} \t {}".format(FP,TN)) print('') print('\t \t \t \t Positive reviews (%)) : ',100*n_pos/len(pairs)) print('\t \t \t \t Success rate (%) : ',100*n_successes/len(pairs)) # overriding getData to only load 1 folder def getData(folder): """ Input: - folder: string of the path of a folder containing txt files. Output: - listdata: list of [Y, X] (e.g. Y = 'Positive', X = "very cool") """ listdata = [] filenames = os.listdir(folder) for filename in filenames[:1]: # change here with open(os.path.join(folder, filename), 'r') as f: for line in f: line2 = line.strip().split('\t') if len(line2) == 2: listdata.append(line2) return listdata def folder2data(train_filename,test_filename,balanced_tr ,balanced_te, n_features): # Entrées : # - train_filename (str) : le nom du **dossier** (et pas le nom du fichier) où se trouvent les instances d'apprentissage # - test_filename (str) : le nom du **dossier** (et pas le nom du fichier) où se trouvent les instances de test # - balanced_tr (bool) : True si l'ensemble d'apprentissage est équilibré; False s'il est laissé tel quel # - balanced_te (bool) : True si l'ensemble de test est équilibré; False s'il est laissé tel quel # - n_features (int) : nombre de variables pour coder chaque instance # Sorties : # - cuple (new_tr_pairs, new_te_pairs): # new_tr_pairs : (Variable(mat), Variable(vect(+1|-1))) ) # new_te_pairs : (list of (Variable(vect), Variable(+1|-1))) tr_te_pairs = {} pairs = getData(train_filename) print(pairs[:2]) if balanced_tr : #Pour un équilibrage 75/25 pairs_using_numbers = [(-1,text) for (target,text) in pairs if (target == 'Negative' or target == 'Neutral')] Positive_reviews = [(1,text) for (target,text) in pairs if target == 'Positive'] pairs_using_numbers += Positive_reviews[:int(len(pairs_using_numbers)*3)] tr_pairs = pairs_using_numbers """ #Pour un équilibrage 50/50 pairs_using_numbers = [(-1,text) for (target,text) in pairs if target == 'Negative'] Positive_reviews = [(1,text) for (target,text) in pairs if target == 'Positive'] pairs_using_numbers += Positive_reviews[:int(len(pairs_using_numbers))] tr_pairs = pairs_using_numbers """ else : pairs_using_numbers = [(1,text) for (target,text) in pairs if target == 'Positive'] pairs_using_numbers += [(-1,text) for (target,text) in pairs if (target == 'Negative' or target == 'Neutral')] tr_pairs = pairs_using_numbers pairs = getData(test_filename) print(pairs[:2]) if balanced_te : pairs_using_numbers = [(-1,text) for (target,text) in pairs if (target == 'Negative' or target == 'Neutral')] Positive_reviews = [(1,text) for (target,text) in pairs if target == 'Positive'] pairs_using_numbers += Positive_reviews[:int(len(pairs_using_numbers))] te_pairs = pairs_using_numbers else : pairs_using_numbers = [(1,text) for (target,text) in pairs if target == 'Positive'] pairs_using_numbers += [(-1,text) for (target,text) in pairs if (target == 'Negative' or target == 'Neutral')] te_pairs = pairs_using_numbers print([text for (_,text) in tr_pairs[:2]]) tfidf_vectorizer = TfidfVectorizer(ngram_range=(1,2)) tfidf_vectorizer.fit([ text for (_,text) in tr_pairs+te_pairs]) # fitting X_tr_token = tfidf_vectorizer.transform([ text for (_,text) in tr_pairs]) X_te_token = tfidf_vectorizer.transform([ text for (_,text) in te_pairs]) truncatedsvd = TruncatedSVD(n_components=n_features) # prépare à projeter les données dans un espace à n_components dimensions truncatedsvd.fit(X_tr_token) truncatedsvd.fit(X_te_token) # Réduction de dimension X_tr_reduced_dim = truncatedsvd.transform(X_tr_token) X_te_reduced_dim = truncatedsvd.transform(X_te_token) print('part de la variance conservée :',sum(truncatedsvd.explained_variance_ratio_)) new_tr_pairs = [Variable(torch.FloatTensor(X_tr_reduced_dim)),Variable(torch.FloatTensor([[note for (note,_) in tr_pairs]]))] new_te_pairs = [] for i in range(len(te_pairs)): (note,_) = te_pairs[i] note = Variable(torch.FloatTensor([[note]])) vect = X_te_reduced_dim[i,:] variable_vect = torch.autograd.Variable(torch.Tensor(vect)) new_te_pairs.append((note,variable_vect)) return new_tr_pairs, new_te_pairs # ================================================================== # ================ Using the MLP in itself ========================= # ================================================================== training_set_folder = "../../data/data_books_training_set" test_set_folder = "../../data/data_videos_testing_set" #test_set_folder = "../../data/data_books_testing_set" n_features = 200 tr_pairs,te_pairs = folder2data(training_set_folder,test_set_folder,balanced_tr = True,balanced_te = True,n_features=n_features) hidden_size = 100 batch_size = tr_pairs[0].data.size()[0] MLP = my_MLP(n_features, hidden_size, batch_size, n_layers = 1) #MLP.evaluateNpairs(te_pairs,1) # show some examples lr = 0.005 N_epochs = 20000 print("learning rate",lr) print(batch_size,'instances') MLP.trainIters( N_epochs,tr_pairs,te_pairs,lr,500,5000) MLP.evaluateRandomly(te_pairs) # show global results torch.save(MLP,'MLP') #cours ; cd 2eme_partie_S9/Transfer_learning/TransferLearningProject/learning/ ; python MLP_base.py print('') print('') print(' Done') print('') print('') print('')
import sys, os, random, pickle, re, time import numpy as np import tensorflow as tf import sklearn.metrics as skm # 0.001 uniform for embeddings, 0.1 for adagrad accumulators, learning rate 0.1, 0.8 class CharacterLSTM(object): def __init__(self, labels, embedding_size=200, lstm_dim=200, optimizer='default', learning_rate='default', embedding_factor = 1.0, decay_rate=1.0, dropout_keep=0.8, num_cores=5): config = tf.ConfigProto() config.gpu_options.allow_growth=True config.inter_op_parallelism_threads=num_cores config.intra_op_parallelism_threads=num_cores self.sess = tf.Session(config=config) self.labels = [] self.embedding_size = embedding_size self.optimizer = optimizer self.decay = decay_rate if optimizer == 'default': self.optimizer = 'rmsprop' else: self.optimizer = optimizer if learning_rate is not 'default': self.lrate = float(learning_rate) else: if self.optimizer in ['adam','rmsprop']: self.lrate = 0.001 elif self.optimizer == 'adagrad': self.lrate = 0.5 else: raise Exception('Unknown optimizer {}'.format(optimizer)) print "Optimizer: {}, Learning rate: {}, Decay rate: {}".format( self.optimizer, self.lrate, self.decay) self.embedding_factor = embedding_factor self.rnn_dim = lstm_dim self.dropout_keep = dropout_keep self.char_buckets = 128 self.labels = labels self._compile() def _compile(self): with self.sess.as_default(): import tensorflow_fold as td output_size = len(self.labels) self.keep_prob = tf.placeholder_with_default(tf.constant(1.0),shape=None) char_emb = td.Embedding(num_buckets=self.char_buckets, num_units_out=self.embedding_size) #initializer=tf.truncated_normal_initializer(stddev=0.15)) char_cell = td.ScopedLayer(tf.contrib.rnn.LSTMCell(num_units=self.rnn_dim), 'char_cell') char_lstm = (td.InputTransform(lambda s: [ord(c) for c in s]) >> td.Map(td.Scalar('int32') >> char_emb) >> td.RNN(char_cell) >> td.GetItem(1) >> td.GetItem(1)) rnn_fwdcell = td.ScopedLayer(tf.contrib.rnn.LSTMCell(num_units=self.rnn_dim), 'lstm_fwd') fwdlayer = td.RNN(rnn_fwdcell) >> td.GetItem(0) rnn_bwdcell = td.ScopedLayer(tf.contrib.rnn.LSTMCell(num_units=self.rnn_dim), 'lstm_bwd') bwdlayer = (td.Slice(step=-1) >> td.RNN(rnn_bwdcell) >> td.GetItem(0) >> td.Slice(step=-1)) pos_emb = td.Embedding(num_buckets=300, num_units_out=32, initializer=tf.truncated_normal_initializer(stddev=0.1)) pos_x = (td.InputTransform(lambda x: x + 150) >> td.Scalar(dtype='int32') >> pos_emb) pos_y = (td.InputTransform(lambda x: x + 150) >> td.Scalar(dtype='int32') >> pos_emb) input_layer = td.Map(td.Record((char_lstm,pos_x,pos_y)) >> td.Concat()) maxlayer = (td.AllOf(fwdlayer, bwdlayer) >> td.ZipWith(td.Concat()) >> td.Max()) output_layer = (input_layer >> maxlayer >> td.FC(output_size, input_keep_prob=self.keep_prob, activation=None)) self.compiler = td.Compiler.create((output_layer, td.Vector(output_size,dtype=tf.int32))) self.y_out, self.y_true = self.compiler.output_tensors self.y_loss = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits( logits=self.y_out,labels=self.y_true)) self.y_prob = tf.nn.softmax(self.y_out) self.y_true_idx = tf.argmax(self.y_true,axis=1) self.y_pred_idx = tf.argmax(self.y_prob,axis=1) self.y_pred = tf.one_hot(self.y_pred_idx,depth=output_size,dtype=tf.int32) epoch_step = tf.Variable(0, trainable=False) self.epoch_step_op = tf.assign(epoch_step, epoch_step+1) lrate_decay = tf.train.exponential_decay(self.lrate, epoch_step, 1, self.decay) if self.optimizer == 'adam': self.opt = tf.train.AdamOptimizer(learning_rate=lrate_decay) elif self.optimizer == 'adagrad': self.opt = tf.train.AdagradOptimizer(learning_rate=lrate_decay, initial_accumulator_value=1e-08) elif self.optimizer == 'rmsprop' or self.optimizer == 'default': self.opt = tf.train.RMSPropOptimizer(learning_rate=lrate_decay, epsilon=1e-08) else: raise Exception(('The optimizer {} is not in list of available ' + 'optimizers: default, adam, adagrad, rmsprop.') .format(self.optimizer)) # apply learning multiplier on on embedding learning rate embeds = [pos_emb.weights, char_emb.weights] grads_and_vars = self.opt.compute_gradients(self.y_loss) found = 0 for i, (grad, var) in enumerate(grads_and_vars): if var in embeds: found += 1 grad = tf.scalar_mul(self.embedding_factor, grad) grads_and_vars[i] = (grad, var) assert found == len(embeds) # internal consistency check self.train_step = self.opt.apply_gradients(grads_and_vars) self.sess.run(tf.global_variables_initializer()) self.saver = tf.train.Saver(max_to_keep=100) def _onehot(self, y, categories): y_onehot = np.zeros((len(y),len(categories))) for i in range(len(y)): y_onehot[i,categories.index(y[i])] = 1 return y_onehot def _train_minibatches(self,minibatches): mavg_loss = None for k, minibatch in enumerate(minibatches): varl = [self.train_step, self.y_loss, self.y_pred_idx, self.y_true_idx] minibatch[self.keep_prob] = self.dropout_keep _, ym_loss, ym_pred, ym_true = self.sess.run(varl, minibatch) if mavg_loss is None: mavg_loss = ym_loss else: mavg_loss = 0.9 * mavg_loss + 0.1 * ym_loss sys.stdout.write(" >> training {}/{} loss={:.7f} \r".format( k+1,len(minibatches),mavg_loss)) sys.stdout.flush() def fit(self, X, y, X_dev, y_dev, num_epoch = 10, batch_size = 32, seed = 1): random.seed(seed) print "Target labels: {}".format(self.labels) trainset = zip(X,self._onehot(y,self.labels)) devset = zip(X_dev,self._onehot(y_dev,self.labels)) trainsp = random.sample(trainset,min(len(trainset)/2,200)) trainfd = self.compiler.build_feed_dict(trainsp) valfd = self.compiler.build_feed_dict(devset) best_epoch = 0 best_model = None best_score = 0 for i in range(1,num_epoch+1): estart = time.time() batchpool = random.sample(trainset,len(trainset)) minibatches = [] for k in range(0,len(batchpool),batch_size): pool = batchpool[k:k+batch_size] minibatches.append(self.compiler.build_feed_dict(pool)) self._train_minibatches(minibatches) self.sess.run(self.epoch_step_op) loss, yt_pred, yt_true = self.sess.run([self.y_loss, self.y_pred_idx, self.y_true_idx], trainfd) f1, precision, recall = self.fscore(yt_pred,yt_true) yv_pred, yv_true = self.sess.run([self.y_pred_idx, self.y_true_idx], valfd) vf1, vprecision, vrecall = self.fscore(yv_pred,yv_true) save_marker = '' if vf1 >= best_score: best_model = '/tmp/model-{}-e{}-s{}.ckpt'.format( type(self).__name__.lower(),i,seed) best_epoch, best_score = i, vf1 self.saver.save(self.sess, best_model) save_marker = '*' elapsed = int(time.time() - estart) emin, esec = elapsed / 60, elapsed % 60 print "epoch {} loss {} fit {:.2f}/{:.2f}/{:.2f} val {:.2f}/{:.2f}/{:.2f} [{}m{}s] {}".format(i, loss, f1, precision, recall, vf1, vprecision, vrecall, emin, esec, save_marker) if best_model is None: print "WARNING: NO GOOD FIT" self.saver.restore(self.sess, best_model) print "Fitted to model from epoch {} with score {} at {}".format(best_epoch,best_score,best_model) def save(self, model_path): self.saver.save(self.sess, model_path) def restore(self, model_path): tf.reset_default_graph() self.saver.restore(self.sess, model_path) def predict(self, X, batch_size = 100): dummy_labels = [self.labels[0]] * len(X) dummy_y = self._onehot(dummy_labels,self.labels) testset_all = zip(X,dummy_y) prediction_idx = [] for k in range(0,len(testset_all),batch_size): testset = testset_all[k:k+batch_size] testfd = self.compiler.build_feed_dict(testset) prediction_idx += list(self.sess.run(self.y_pred_idx, testfd)) return [ self.labels[idx] for idx in prediction_idx ] def predict_proba(self, X, batch_size = 100): dummy_labels = [self.labels[0]] * len(X) dummy_y = self._onehot(dummy_labels,self.labels) testset_all = zip(X,dummy_y) y_prob_list = [] for k in range(0,len(testset_all),batch_size): testset = testset_all[k:k+batch_size] testfd = self.compiler.build_feed_dict(testset) y_prob_list.append(self.sess.run(self.y_prob, testfd)) return np.concatenate(y_prob_list,axis=0) def evaluate(self,X,y, batch_size = 100, macro = False): testset_all = zip(X,self._onehot(y,self.labels)) y_pred_idx = [] y_true_idx = [] for k in range(0,len(testset_all),batch_size): testset = testset_all[k:k+batch_size] testfd = self.compiler.build_feed_dict(testset) yp, yt = self.sess.run([self.y_pred_idx,self.y_true_idx], testfd) y_pred_idx += list(yp) y_true_idx += list(yt) return self.fscore(y_pred_idx,y_true_idx,macro) def fscore(self,y_pred,y_true, macro=False): avg_meth = 'micro' if macro: avg_meth = 'macro' labels = [ i for i in range(len(self.labels)) if self.labels[i] not in ['false','False', False, None, 'other', 'OTHER' ] ] f = skm.f1_score(y_true, y_pred, average=avg_meth,labels=labels) p = skm.precision_score(y_true, y_pred, average=avg_meth,labels=labels) r = skm.recall_score(y_true, y_pred, average=avg_meth,labels=labels) return f, p ,r
# Licensed under a 3-clause BSD style license - see LICENSE.rst from glue.core import Hub, HubListener, Data, DataCollection from glue.core.message import (DataCollectionAddMessage, DataAddComponentMessage, SettingsChangeMessage) from .layout import CubeVizLayout CUBEVIZ_LAYOUT = 'cubeviz_layout' class CubevizManager(HubListener): def __init__(self, session): self._session = session self._hub = session.hub self._app = session.application self._layout = None self._empty_layout = self._app.add_fixed_layout_tab(CubeVizLayout) self._app.close_tab(0, warn=False) self.hide_sidebar() self._hub.subscribe( self, DataCollectionAddMessage, handler=self.handle_new_dataset) self._hub.subscribe( self, DataAddComponentMessage, handler=self.handle_new_component) self._hub.subscribe( self, SettingsChangeMessage, handler=self.handle_settings_change) # Look for any cube data files that were loaded from the command line for data in session.data_collection: if data.meta.get(CUBEVIZ_LAYOUT, ''): self.configure_layout(data) def handle_new_dataset(self, message): data = message.data if data.meta.get(CUBEVIZ_LAYOUT, ''): self.configure_layout(data) def configure_layout(self, data): # Assume for now the data is not yet in any tab if self._empty_layout is not None: cubeviz_layout = self._empty_layout else: cubeviz_layout = self._app.add_fixed_layout_tab(CubeVizLayout) try: self.setup_data(cubeviz_layout, data) finally: self._empty_layout = None def handle_new_component(self, message): #self._layout.add_new_data_component(str(message.component_id)) self._layout.add_new_data_component(message.component_id) def handle_settings_change(self, message): if self._layout is not None: self._layout._handle_settings_change(message) def hide_sidebar(self): self._app._ui.main_splitter.setSizes([0, 300]) def setup_data(self, cubeviz_layout, data): # Automatically add data to viewers and set attribute for split viewers image_viewers = [cubeviz_layout.single_view._widget, cubeviz_layout.left_view._widget, cubeviz_layout.middle_view._widget, cubeviz_layout.right_view._widget] data_headers = [str(x).strip() for x in data.component_ids() if not x in data.coordinate_components] # Single viewer should display FLUX only by default image_viewers[0].add_data(data) image_viewers[0].state.aspect = 'auto' image_viewers[0].state.layers[0].attribute = data.id[data_headers[0]] # Split image viewers should each show different component by default print('data headers {}'.format(data_headers)) for ii, view in enumerate(image_viewers[1:]): view.add_data(data) view.state.aspect = 'auto' #view.state.layers[0].attribute = data.id[data_headers[ii%len(data_headers)]] view.state.layers[0].attribute = data.id['FLUX'] # Disable any viewers that are beyond the number of data acomponents #for ii in range(len(data_headers), 3): # image_viewers[1+ii].setEnabled(False) cubeviz_layout.add_data(data) index = self._app.get_tab_index(cubeviz_layout) self._app.tab_bar.rename_tab(index, "CubeViz: {}".format(data.label)) self._layout = cubeviz_layout
<reponame>Json0926/object_detection import os import cv2 import time import argparse import numpy as np import tensorflow as tf from utils.webcam import FPS, WebcamVideoStream from queue import Queue from threading import Thread from analytics.tracking import ObjectTracker from video_writer import VideoWriter from detect_object import detect_objects CWD_PATH = os.getcwd() MODEL_NAME = 'faster_rcnn_nas_coco_2018_01_28' PATH_TO_MODEL = os.path.join(CWD_PATH, 'detection', 'tf_models', MODEL_NAME, 'frozen_inference_graph.pb') PATH_TO_VIDEO = os.path.join(CWD_PATH, 'vlog1.mp4') def worker(input_q, output_q): # load the frozen tensorflow model into memory detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_MODEL, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') sess = tf.Session(graph=detection_graph) fps = FPS().start() while True: fps.update() frame = input_q.get() # frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) output_q.put(detect_objects(frame, sess, detection_graph)) fps.stop() sess.close() if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-src', '--source', dest='video_source', type=int, default=0, help='Device index of the camera.') parser.add_argument('-wd', '--width', dest='width', type=int, default=1280, help='Width of the frames in the video stream.') parser.add_argument('-ht', '--height', dest='height', type=int, default=720, help='Height of the frames in the video stream.') args = parser.parse_args() input_q = Queue(5) output_q = Queue() for i in range(1): t = Thread(target=worker, args=(input_q, output_q)) t.daemon = True t.start() input_video = 'rtsp://admin:<PASSWORD>@192.168.0.4:554/openUrl/GXKelBC' # cap = cv2.VideoCapture(input_video) video_capture = WebcamVideoStream(src=input_video, width=args.width, height=args.height).start() writer = VideoWriter('output.mp4', (args.width, args.height)) ''' stream = cv2.VideoCapture(0) stream.set(cv2.CAP_PROP_FRAME_WIDTH, args.width) stream.set(cv2.CAP_PROP_FRAME_HEIGHT, args.height) ''' fps = FPS().start() object_tracker = ObjectTracker(path='./', file_name='report.csv') while True: frame = video_capture.read() # (ret, frame) = stream.read() fps.update() if fps.get_numFrames() % 2 != 0: continue # put data into the input queue input_q.put(frame) t = time.time() if output_q.empty(): pass # fill up queue else: data = output_q.get() context = {'frame': frame, 'class_names': data['class_names'], 'rec_points': data['rect_points'], 'class_colors': data['class_colors'], 'width': args.width, 'height': args.height, 'frame_number': fps.get_numFrames()} new_frame = object_tracker(context) writer(new_frame) cv2.imshow('Video', new_frame) print('[INFO] elapsed time: {:.2f}'.format(time.time() - t)) if cv2.waitKey(1) & 0xFF == ord('q'): break fps.stop() print('[INFO] elapsed time (total): {:.2f}'.format(fps.elapsed())) print('[INFO] approx. FPS: {:.2f}'.format(fps.fps())) video_capture.stop() writer.close() cv2.destroyAllWindows()
<reponame>jaxsenh/the-devil-that-lurks # creates datagrams to send to server from direct.distributed.PyDatagram import PyDatagram from communications.codes import * # General def dg_kill_connection(): dg = PyDatagram() dg.addUint8(KILLED_CONNECTION) return dg # Main menu def dg_deliver_pid(pid): dg = PyDatagram() dg.addUint8(DELIVER_PID) dg.addUint16(pid) return dg def dg_deliver_game(game): dg = PyDatagram() dg.addUint8(DELIVER_GAME) dg.addUint16(game.gid) dg.addUint8(game.get_vote_count()) return dg def dg_kick_from_game(reason): dg = PyDatagram() dg.addUint8(KICKED_FROM_GAME) dg.addUint8(reason) return dg # Lobby def dg_add_player(local_id, name="???"): """ Tells players in lobby that a new player has joined uint8 - local_id string - name """ dg = PyDatagram() dg.addUint8(ADD_PLAYER) dg.addUint8(local_id) dg.addString(name) return dg def dg_remove_player(local_id): """ Tells the players that a player has Left. This should only be called while the game is in the lobby as it'll delete the player's data from clients :param local_id: the local ID of the ex-player :type local_id: int :return: the datagram to send :rtype: pydatagram """ dg = PyDatagram() dg.addUint8(REMOVE_PLAYER) dg.addUint8(local_id) return dg def dg_update_vote_count(num): dg = PyDatagram() dg.addUint8(UPDATE_VOTE_COUNT) dg.addUint8(num) return dg def dg_update_player_name(local_id, new_name): """ Tells players that someone has updated their name :param local_id: the local ID of the player changing their name :type local_id: int :param new_name: the new name of the player :type new_name: string :return: the datagram to send :rtype: pydatagram """ dg = PyDatagram() dg.addUint8(UPDATE_NAME) dg.addUint8(local_id) dg.addString(new_name) return dg def dg_start_game(game): dg = PyDatagram() dg.addUint8(START_GAME) return dg # Game def dg_goto_day(game): dg = PyDatagram() dg.addUint8(GOTO_DAY) dg.addUint8(game.day_count) dg.addUint8(game.red_room) return dg def dg_goto_night(game): dg = PyDatagram() dg.addUint8(GOTO_NIGHT) return dg def dg_has_died(local_id): """ :param local_id: The local_id of the player who has died :type local_id: Uint8 """ dg = PyDatagram() dg.addUint8(HAS_DIED) dg.addUint8(local_id) return dg def dg_you_are_killer(): dg = PyDatagram() dg.addUint8(YOU_ARE_KILLER) return dg def dg_kill_failed_empty_room(): dg = PyDatagram() dg.addUint8(KILL_FAILED_EMPTY_ROOM) return dg def dg_how_many_in_room(num): dg = PyDatagram() dg.addUint8(NUM_IN_ROOM) dg.addUint8(num) return dg
"""Bundles common gui functions and classes When creating a standalone app, you can use :func:`jukeboxcore.gui.main.init_gui` to make sure there is a running QApplication. Usually the launcher will do that for you. Then use set_main_style to apply the main_stylesheet to your app. That way all the plugins have a consistent look. """ import os import weakref import pkg_resources import pkgutil import sys try: import shiboken except ImportError: from PySide import shiboken from PySide import QtGui, QtCore from jukeboxcore.log import get_logger log = get_logger(__name__) from jukeboxcore.constants import MAIN_STYLESHEET, ICON_PATH from jukeboxcore.gui import resources app = None """The QApplication app instance when using :func:`jukebox.core.gui.main.get_qapp`""" def get_qapp(): """Return an instance of QApplication. Creates one if neccessary. :returns: a QApplication instance :rtype: QApplication :raises: None """ global app app = QtGui.QApplication.instance() if app is None: app = QtGui.QApplication([], QtGui.QApplication.GuiClient) return app def load_all_resources(): """Load all resources inside this package When compiling qt resources, the compiled python file will register the resource on import. .. Warning:: This will simply import all modules inside this package """ pkgname = resources.__name__ for importer, mod_name, _ in pkgutil.iter_modules(resources.__path__): full_mod_name = '%s.%s' % (pkgname, mod_name) if full_mod_name not in sys.modules: module = importer.find_module(mod_name ).load_module(full_mod_name) log.debug("Loaded resource from: %s", module) def set_main_style(widget): """Load the main.qss and apply it to the application :param widget: The widget to apply the stylesheet to. Can also be a QApplication. ``setStylesheet`` is called on the widget. :type widget: :class:`QtGui.QWidget` :returns: None :rtype: None :raises: None """ load_all_resources() with open(MAIN_STYLESHEET, 'r') as qss: sheet = qss.read() widget.setStyleSheet(sheet) def init_gui(): """Initialize a QApplication and apply the main style to it :returns: None :rtype: None :raises: None """ app = get_qapp() app.setStyle("plastique") set_main_style(app) def wrap(ptr, base=None): """Wrap the given pointer with shiboken and return the appropriate QObject :returns: if ptr is not None returns a QObject that is cast to the appropriate class :rtype: QObject | None :raises: None """ if ptr is None: return None ptr = long(ptr) # Ensure type if base is None: qObj = shiboken.wrapInstance(long(ptr), QtCore.QObject) metaObj = qObj.metaObject() cls = metaObj.className() superCls = metaObj.superClass().className() if hasattr(QtGui, cls): base = getattr(QtGui, cls) elif hasattr(QtGui, superCls): base = getattr(QtGui, superCls) else: base = QtGui.QWidget return shiboken.wrapInstance(long(ptr), base) def dt_to_qdatetime(dt): """Convert a python datetime.datetime object to QDateTime :param dt: the datetime object :type dt: :class:`datetime.datetime` :returns: the QDateTime conversion :rtype: :class:`QtCore.QDateTime` :raises: None """ return QtCore.QDateTime(QtCore.QDate(dt.year, dt.month, dt.day), QtCore.QTime(dt.hour, dt.minute, dt.second)) def get_icon(name, aspix=False, asicon=False): """Return the real file path to the given icon name If aspix is True return as QtGui.QPixmap, if asicon is True return as QtGui.QIcon. :param name: the name of the icon :type name: str :param aspix: If True, return a QtGui.QPixmap. :type aspix: bool :param asicon: If True, return a QtGui.QIcon. :type asicon: bool :returns: The real file path to the given icon name. If aspix is True return as QtGui.QPixmap, if asicon is True return as QtGui.QIcon. If both are True, a QtGui.QIcon is returned. :rtype: string :raises: None """ datapath = os.path.join(ICON_PATH, name) icon = pkg_resources.resource_filename('jukeboxcore', datapath) if aspix or asicon: icon = QtGui.QPixmap(icon) if asicon: icon = QtGui.QIcon(icon) return icon class JB_Gui(object): """A mixin class for top-level widgets liek main windows, widgets, dialogs etc .. Important: If you use this widget with a Qt object, make sure that your class will inherit from JB_Gui first! Qt objects do not call the super constructor! This class tracks its instances. So all classes that use this mixedin are tracked. Additionally each class that uses this mixin keeps track of its own instances and instances of its own class+subclasses """ _allinstances = set() def __init__(self, *args, **kwargs): """Constructs a new JB_Gui that will be tracked :raises: None """ super(JB_Gui, self).__init__(*args, **kwargs) self._add_instance() def _add_instance(self): JB_Gui._allinstances.add(self) @classmethod def allinstances(cls): """Return all instances that inherit from JB_Gui :returns: all instances that inherit from JB_Gui :rtype: list :raises: None """ JB_Gui._allinstances = weakref.WeakSet([i for i in cls._allinstances if shiboken.isValid(i)]) return list(cls._allinstances) @classmethod def classinstances(cls): """Return all instances of the current class JB_Gui will not return the instances of subclasses A subclass will only return the instances that have the same type as the subclass. So it won\'t return instances of further subclasses. :returns: all instnaces of the current class :rtype: list :raises: None """ l = [i for i in cls.allinstances() if type(i) == cls] return l @classmethod def instances(cls): """Return all instances of this class and subclasses :returns: all instances of the current class and subclasses :rtype: list :raises: None """ l = [i for i in cls.allinstances() if isinstance(i, cls)] return l class JB_MainWindow(JB_Gui, QtGui.QMainWindow): """A main window class that should be used for all main windows It is useful for tracking all main windows and we can already set common attributes. """ def __init__(self, *args, **kwargs): """Constructs a new JB_MainWindow. Arguments are passed on to QMainWindow :raises: None """ super(JB_MainWindow, self).__init__(*args, **kwargs) set_main_style(self) self.setAttribute(QtCore.Qt.WA_DeleteOnClose, on=True) jb_icon = get_icon('JB_Icon_32x32.png',asicon=True) self.setWindowIcon(jb_icon) class JB_Dialog(JB_Gui, QtGui.QDialog): """A dialog class that should be used for all generic dialogs It is useful for tracking all dialogs and we can already set common attributes. """ def __init__(self, *args, **kwargs): """Constructs a new JB_MainWindow. Arguments are passed on to QMainWindow :raises: None """ super(JB_Dialog, self).__init__(*args, **kwargs) set_main_style(self) jb_icon = get_icon('JB_Icon_32x32.png',asicon=True) self.setAttribute(QtCore.Qt.WA_DeleteOnClose, on=True) self.setWindowIcon(jb_icon)
""" Tests of the src.sample_data module. """ import numpy as np import pandas as pd import pytest from collections import namedtuple from src import sample_data SampleDataParameters = namedtuple("SampleDataParameters", ["step_order", "n", "i_max", "v_min", "v_max"]) @pytest.fixture() def small_sample_conditions(): """Returns a step_order and number of dpts per step, etc for testing.""" n = 3 step_order = [ (1, 'C'), (1, 'RAC'), (2, 'D') ] return SampleDataParameters(step_order=step_order, n=n, i_max=1, v_min=2, v_max=3) @pytest.fixture() def small_sample_data(): """Returns a dataframe of expected sample_data. Notes ----- Complies with parameters in small_sample_conditions. """ return pd.DataFrame(data={ "dpt": [1, 2, 3, 4, 5, 6, 7, 8, 9], "cyc": [1, 1, 1, 1, 1, 1, 2, 2, 2], "stp": [1, 1, 1, 2, 2, 2, 3, 3, 3], "cur": [1.0, 1.0, 1.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0], "pot": [2.0, 2.5, 3.0, 3.0, 3.0, 3.0, 3.0, 2.5, 2.0], "time": [0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5], "start": [170] * 9, }) @pytest.mark.parametrize( "last_val,ndpts,expected", [ (5, 3, np.array([6, 7, 8])), (1, 2, np.array([2, 3])), ] ) def test_get_step_dpt_data(last_val, ndpts, expected): """Tests the _get_step_dpt_data method. - for building dpt vals in a step.""" assert (sample_data._get_step_dpt_data(last_val, ndpts) == expected).all() def test_get_dpt_data(small_sample_conditions, small_sample_data): """Tests the _get_dpt_data method.""" assert (sample_data._get_dpt_data(small_sample_conditions.step_order, small_sample_conditions.n) == small_sample_data["dpt"].values).all() def test_get_step_stp_idx_data(): """Tests the _get_step_stp_idx_data method.""" assert (sample_data._get_step_stp_idx_data("C", 4, {"C": 1}) == np.array([1, 1, 1, 1])).all() def test_get_stp_data(small_sample_conditions, small_sample_data): """Tests the _get_stp_data method.""" assert (sample_data._get_stp_data(small_sample_conditions.step_order, small_sample_conditions.n) == small_sample_data["stp"].values).all() def test_get_step_cyc_data(): """Tests the _get_step_cyc_data method.""" assert (sample_data._get_step_cyc_data(2, 4) == np.array([2, 2, 2, 2])).all() def test_get_cyc_data(small_sample_conditions, small_sample_data): """Tests the _get_cyc_data method.""" assert (sample_data._get_cyc_data(small_sample_conditions.step_order, small_sample_conditions.n) == small_sample_data["cyc"].values).all() @pytest.mark.parametrize( "step_code,ndpts,v_min,v_max,expected", [ ("C", 3, 0, 1, np.array([0, 0.5, 1.0])), ("D", 5, 0, 1, np.array([1.0, 0.75, 0.50, 0.25, 0.0])), ("RAC", 2, 0, 1, np.array([1.0, 1.0])), ("RAD", 2, 0, 1, np.array([0.0, 0.0])), ] ) def test_get_step_pot_data(step_code, ndpts, v_min, v_max, expected): """Tests the _get_step_pot_data method. - for building dpt vals in a step.""" assert ( sample_data._get_step_pot_data( step_code, n=ndpts, v_min=v_min, v_max=v_max, ) == expected ).all() def test_get_pot_data(small_sample_conditions, small_sample_data): """Tests the _get_pot_data method.""" assert ( sample_data._get_pot_data( small_sample_conditions.step_order, n=small_sample_conditions.n, v_min=small_sample_conditions.v_min, v_max=small_sample_conditions.v_max, ) == small_sample_data["pot"].values ).all() @pytest.mark.parametrize( "step_code,ndpts,i_max,expected", [ ("C", 3, 1, np.array([1.0, 1.0, 1.0])), ("D", 5, 3, np.array([-3.0, -3.0, -3.0, -3.0, -3.0])), ("RAC", 2, 1, np.array([0.0, 0.0])), ("RAD", 2, 1, np.array([0.0, 0.0])), ] ) def test_get_step_cur_data(step_code, ndpts, i_max, expected): """Tests the _get_step_cur_data method. - for building dpt vals in a step.""" assert ( sample_data._get_step_cur_data( step_code, n=ndpts, i_max=i_max, ) == expected ).all() def test_get_cur_data(small_sample_conditions, small_sample_data): """Tests the _get_cur_data method.""" assert ( sample_data._get_cur_data( small_sample_conditions.step_order, n=small_sample_conditions.n, i_max=small_sample_conditions.i_max ) == small_sample_data["cur"].values ).all() def test_create_data(small_sample_conditions, small_sample_data): """Tests the create_data method.""" actual = sample_data.create_data( step_order=small_sample_conditions.step_order, n=small_sample_conditions.n, v_min=small_sample_conditions.v_min, v_max=small_sample_conditions.v_max, i_max=small_sample_conditions.i_max, ) assert (actual == small_sample_data).all().all()
""" Copyright (c) 2017 Red Hat, Inc All rights reserved. This software may be modified and distributed under the terms of the BSD license. See the LICENSE file for details. """ from __future__ import unicode_literals, absolute_import from jsonschema import ValidationError import io import logging import os import pkg_resources import pytest from textwrap import dedent import re import yaml import smtplib from copy import deepcopy import atomic_reactor import koji from atomic_reactor.core import ContainerTasker from atomic_reactor.inner import DockerBuildWorkflow from atomic_reactor.util import read_yaml import atomic_reactor.utils.cachito import atomic_reactor.utils.koji import atomic_reactor.utils.odcs import osbs.conf import osbs.api from osbs.utils import RegistryURI from osbs.exceptions import OsbsValidationException from atomic_reactor.plugins.pre_reactor_config import (ReactorConfig, ReactorConfigPlugin, get_config, WORKSPACE_CONF_KEY, get_koji_session, get_koji_path_info, get_odcs_session, get_smtp_session, get_openshift_session, get_clusters_client_config_path, get_docker_registry, get_platform_to_goarch_mapping, get_goarch_to_platform_mapping, get_default_image_build_method, get_buildstep_alias, get_flatpak_base_image, get_flatpak_metadata, get_cachito_session, get_operator_manifests, CONTAINER_DEFAULT_BUILD_METHOD, get_build_image_override, NO_FALLBACK) from tests.constants import TEST_IMAGE, REACTOR_CONFIG_MAP from tests.docker_mock import mock_docker from tests.stubs import StubInsideBuilder from flexmock import flexmock class TestReactorConfigPlugin(object): def prepare(self): mock_docker() tasker = ContainerTasker() workflow = DockerBuildWorkflow( TEST_IMAGE, source={'provider': 'git', 'uri': 'asd'}, ) workflow.builder = StubInsideBuilder() workflow.builder.tasker = tasker return tasker, workflow @pytest.mark.parametrize(('fallback'), [ False, True ]) @pytest.mark.parametrize(('config', 'valid'), [ ("""\ version: 1 registries: - url: https://container-registry.example.com/v2 auth: cfg_path: /var/run/secrets/atomic-reactor/v2-registry-dockercfg """, True), ("""\ version: 1 registries: - url: https://container-registry.example.com/v2 auth: cfg_path: /var/run/secrets/atomic-reactor/v2-registry-dockercfg - url: https://another-container-registry.example.com/ auth: cfg_path: /var/run/secrets/atomic-reactor/another-registry-dockercfg """, True), ("""\ version: 1 registries: - url: https://old-container-registry.example.com/v1 auth: cfg_path: /var/run/secrets/atomic-reactor/v1-registry-dockercfg """, False), ]) def test_get_docker_registry(self, config, fallback, valid): _, workflow = self.prepare() workflow.plugin_workspace[ReactorConfigPlugin.key] = {} config_json = read_yaml(config, 'schemas/config.json') docker_reg = { 'version': 'v2', 'insecure': False, 'secret': '/var/run/secrets/atomic-reactor/v2-registry-dockercfg', 'url': 'https://container-registry.example.com/v2', } if fallback: if valid: docker_fallback = docker_reg expected = docker_reg else: docker_fallback = NO_FALLBACK else: docker_fallback = {} expected = { 'url': 'https://container-registry.example.com', 'insecure': False, 'secret': '/var/run/secrets/atomic-reactor/v2-registry-dockercfg' } workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] =\ ReactorConfig(config_json) if valid: docker_registry = get_docker_registry(workflow, docker_fallback) assert docker_registry == expected else: if fallback: with pytest.raises(KeyError): get_docker_registry(workflow, docker_fallback) else: with pytest.raises(OsbsValidationException): get_docker_registry(workflow, docker_fallback) def test_no_config(self): _, workflow = self.prepare() conf = get_config(workflow) assert isinstance(conf, ReactorConfig) same_conf = get_config(workflow) assert conf is same_conf @pytest.mark.parametrize('basename', ['reactor-config.yaml', None]) def test_filename(self, tmpdir, basename): filename = os.path.join(str(tmpdir), basename or 'config.yaml') with open(filename, 'w'): pass tasker, workflow = self.prepare() plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir), basename=filename) assert plugin.run() is None def test_filename_not_found(self): tasker, workflow = self.prepare() os.environ.pop('REACTOR_CONFIG', None) plugin = ReactorConfigPlugin(tasker, workflow, config_path='/not-found') with pytest.raises(Exception): plugin.run() def test_no_schema_resource(self, tmpdir, caplog): class FakeProvider(object): def get_resource_stream(self, pkg, rsc): raise IOError # pkg_resources.resource_stream() cannot be mocked directly # Instead mock the module-level function it calls. (flexmock(pkg_resources) .should_receive('get_provider') .and_return(FakeProvider())) filename = os.path.join(str(tmpdir), 'config.yaml') with open(filename, 'w'): pass tasker, workflow = self.prepare() plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir)) with caplog.at_level(logging.ERROR), pytest.raises(Exception): plugin.run() captured_errs = [x.message for x in caplog.records] assert "unable to extract JSON schema, cannot validate" in captured_errs @pytest.mark.parametrize('schema', [ # Invalid JSON '{', # Invalid schema '{"properties": {"any": null}}', ]) def test_invalid_schema_resource(self, tmpdir, caplog, schema): class FakeProvider(object): def get_resource_stream(self, pkg, rsc): return io.BufferedReader(io.BytesIO(schema)) # pkg_resources.resource_stream() cannot be mocked directly # Instead mock the module-level function it calls. (flexmock(pkg_resources) .should_receive('get_provider') .and_return(FakeProvider())) filename = os.path.join(str(tmpdir), 'config.yaml') with open(filename, 'w'): pass tasker, workflow = self.prepare() plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir)) with caplog.at_level(logging.ERROR), pytest.raises(Exception): plugin.run() captured_errs = [x.message for x in caplog.records] assert any("cannot validate" in x for x in captured_errs) @pytest.mark.parametrize(('config', 'errors'), [ ("""\ clusters: foo: - name: bar max_concurrent_builds: 1 """, [ "validation error (at top level): " "%r is a required property" % u'version', ]), ("""\ version: 1 clusters: foo: bar: 1 plat/form: - name: foo max_concurrent_builds: 1 """, [ "validation error (clusters.foo): None is not of type %r" % u'array', "validation error (clusters.bar): 1 is not of type %r" % u'array', re.compile(r"validation error \(clusters\): .*'plat/form'"), ]), ("""\ version: 1 clusters: foo: - name: 1 max_concurrent_builds: 1 - name: blah max_concurrent_builds: one - name: "2" # quoting prevents error max_concurrent_builds: 2 - name: negative max_concurrent_builds: -1 """, [ "validation error (clusters.foo[0].name): " "1 is not of type %r" % u'string', "validation error (clusters.foo[1].max_concurrent_builds): " "'one' is not of type %r" % u'integer', re.compile(r"validation error \(clusters\.foo\[3\]\.max_concurrent_builds\): -1(\.0)?" r" is less than the minimum of 0"), ]), ("""\ version: 1 clusters: foo: - name: blah max_concurrent_builds: 1 enabled: never """, [ "validation error (clusters.foo[0].enabled): " "'never' is not of type %r" % u'boolean', ]), ("""\ version: 1 clusters: foo: # missing name - nam: bar max_concurrent_builds: 1 # missing max_concurrent_builds - name: baz max_concurrrent_builds: 2 - name: bar max_concurrent_builds: 4 extra: false """, [ "validation error (clusters.foo[0]): " "%r is a required property" % u'name', "validation error (clusters.foo[1]): " "%r is a required property" % u'max_concurrent_builds', "validation error (clusters.foo[2]): " "Additional properties are not allowed ('extra' was unexpected)", ]) ]) def test_bad_cluster_config(self, tmpdir, caplog, reactor_config_map, config, errors): if reactor_config_map: os.environ['REACTOR_CONFIG'] = dedent(config) else: filename = os.path.join(str(tmpdir), 'config.yaml') with open(filename, 'w') as fp: fp.write(dedent(config)) tasker, workflow = self.prepare() plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir)) with caplog.at_level(logging.ERROR), pytest.raises(ValidationError): plugin.run() os.environ.pop('REACTOR_CONFIG', None) captured_errs = [x.message for x in caplog.records] for error in errors: try: # Match regexp assert any(filter(error.match, captured_errs)) except AttributeError: # String comparison assert error in captured_errs def test_bad_version(self, tmpdir): filename = os.path.join(str(tmpdir), 'config.yaml') with open(filename, 'w') as fp: fp.write("version: 2") tasker, workflow = self.prepare() plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir)) with pytest.raises(ValueError): plugin.run() @pytest.mark.parametrize(('config', 'clusters'), [ # Empty config ("", []), # Built-in default config (yaml.dump(ReactorConfig.DEFAULT_CONFIG), []), # Unknown key ("""\ version: 1 special: foo """, []), ("""\ version: 1 clusters: ignored: - name: foo max_concurrent_builds: 2 platform: - name: one max_concurrent_builds: 4 - name: two max_concurrent_builds: 8 enabled: true - name: three max_concurrent_builds: 16 enabled: false """, [ ('one', 4), ('two', 8), ]), ]) def test_good_cluster_config(self, tmpdir, reactor_config_map, config, clusters): if reactor_config_map and config: os.environ['REACTOR_CONFIG'] = dedent(config) else: filename = os.path.join(str(tmpdir), 'config.yaml') with open(filename, 'w') as fp: fp.write(dedent(config)) tasker, workflow = self.prepare() plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir)) assert plugin.run() is None os.environ.pop('REACTOR_CONFIG', None) conf = get_config(workflow) enabled = conf.get_enabled_clusters_for_platform('platform') assert set([(x.name, x.max_concurrent_builds) for x in enabled]) == set(clusters) @pytest.mark.parametrize(('extra_config', 'fallback', 'error'), [ ('clusters_client_config_dir: /the/path', None, None), ('clusters_client_config_dir: /the/path', '/unused/path', None), (None, '/the/path', None), (None, NO_FALLBACK, KeyError), ]) def test_cluster_client_config_path(self, tmpdir, reactor_config_map, extra_config, fallback, error): config = 'version: 1' if extra_config: config += '\n' + extra_config if reactor_config_map and config: os.environ['REACTOR_CONFIG'] = config else: filename = os.path.join(str(tmpdir), 'config.yaml') with open(filename, 'w') as fp: fp.write(config) tasker, workflow = self.prepare() plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir)) assert plugin.run() is None os.environ.pop('REACTOR_CONFIG', None) if error: with pytest.raises(error): get_clusters_client_config_path(workflow, fallback) else: path = get_clusters_client_config_path(workflow, fallback) assert path == '/the/path/osbs.conf' @pytest.mark.parametrize('default', ( 'release', 'beta', 'unsigned', )) def test_odcs_config(self, tmpdir, default): filename = str(tmpdir.join('config.yaml')) with open(filename, 'w') as fp: fp.write(dedent("""\ version: 1 odcs: signing_intents: - name: release keys: [R123, R234] - name: beta keys: [<KEY> - name: unsigned keys: [] default_signing_intent: {default} api_url: http://odcs.example.com auth: ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret """.format(default=default))) tasker, workflow = self.prepare() plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir)) assert plugin.run() is None odcs_config = get_config(workflow).get_odcs_config() assert odcs_config.default_signing_intent == default unsigned_intent = {'name': 'unsigned', 'keys': [], 'restrictiveness': 0} beta_intent = {'name': 'beta', 'keys': ['<KEY>'], 'restrictiveness': 1} release_intent = {'name': 'release', 'keys': ['R123', 'R234'], 'restrictiveness': 2} assert odcs_config.signing_intents == [ unsigned_intent, beta_intent, release_intent ] assert odcs_config.get_signing_intent_by_name('release') == release_intent assert odcs_config.get_signing_intent_by_name('beta') == beta_intent assert odcs_config.get_signing_intent_by_name('unsigned') == unsigned_intent with pytest.raises(ValueError): odcs_config.get_signing_intent_by_name('missing') assert odcs_config.get_signing_intent_by_keys(['R123', 'R234'])['name'] == 'release' assert odcs_config.get_signing_intent_by_keys('R123 R234')['name'] == 'release' assert odcs_config.get_signing_intent_by_keys(['R123'])['name'] == 'release' assert odcs_config.get_signing_intent_by_keys('R123')['name'] == 'release' assert odcs_config.get_signing_intent_by_keys(['R123', 'B456'])['name'] == 'beta' assert odcs_config.get_signing_intent_by_keys(['B456', 'R123'])['name'] == 'beta' assert odcs_config.get_signing_intent_by_keys('B456 R123')['name'] == 'beta' assert odcs_config.get_signing_intent_by_keys('<KEY> ')['name'] == 'beta' assert odcs_config.get_signing_intent_by_keys(['B456'])['name'] == 'beta' assert odcs_config.get_signing_intent_by_keys('B456')['name'] == 'beta' assert odcs_config.get_signing_intent_by_keys([])['name'] == 'unsigned' assert odcs_config.get_signing_intent_by_keys('')['name'] == 'unsigned' with pytest.raises(ValueError): assert odcs_config.get_signing_intent_by_keys(['missing']) with pytest.raises(ValueError): assert odcs_config.get_signing_intent_by_keys(['R123', 'R234', 'B457']) def test_odcs_config_invalid_default_signing_intent(self, tmpdir): filename = str(tmpdir.join('config.yaml')) with open(filename, 'w') as fp: fp.write(dedent("""\ version: 1 odcs: signing_intents: - name: release keys: [R123] - name: beta keys: [R123, B456] - name: unsigned keys: [] default_signing_intent: spam api_url: http://odcs.example.com auth: ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret """)) tasker, workflow = self.prepare() plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir)) assert plugin.run() is None with pytest.raises(ValueError) as exc_info: get_config(workflow).get_odcs_config() message = str(exc_info.value) assert message == dedent("""\ unknown signing intent name "spam", valid names: unsigned, beta, release """.rstrip()) def test_odcs_config_deprecated_signing_intent(self, tmpdir, caplog): filename = str(tmpdir.join('config.yaml')) with open(filename, 'w') as fp: fp.write(dedent("""\ version: 1 odcs: signing_intents: - name: release keys: [R123] deprecated_keys: [R122] default_signing_intent: release api_url: http://odcs.example.com auth: ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret """)) tasker, workflow = self.prepare() plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir)) assert plugin.run() is None odcs_config = get_config(workflow).get_odcs_config() signing_intent = odcs_config.get_signing_intent_by_keys(['R123']) assert signing_intent['name'] == 'release' assert 'contain deprecated entries' not in caplog.text signing_intent = odcs_config.get_signing_intent_by_keys(['R123', 'R122']) assert signing_intent['name'] == 'release' assert 'contain deprecated entries' in caplog.text @pytest.mark.parametrize('fallback', (True, False, None)) @pytest.mark.parametrize('method', [ 'koji', 'odcs', 'smtp', 'arrangement_version', 'artifacts_allowed_domains', 'yum_repo_allowed_domains', 'image_labels', 'image_label_info_url_format', 'image_equal_labels', 'fail_on_digest_mismatch', 'openshift', 'group_manifests', 'platform_descriptors', 'prefer_schema1_digest', 'content_versions', 'registries', 'yum_proxy', 'source_registry', 'sources_command', 'required_secrets', 'worker_token_secrets', 'clusters', 'hide_files', 'skip_koji_check_for_base_image', 'deep_manifest_list_inspection' ]) def test_get_methods(self, fallback, method): _, workflow = self.prepare() workflow.plugin_workspace[ReactorConfigPlugin.key] = {} if fallback is False: workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] = \ ReactorConfig(yaml.safe_load(REACTOR_CONFIG_MAP)) else: if fallback: fall_source = ReactorConfig(yaml.safe_load(REACTOR_CONFIG_MAP)) else: fall_source = ReactorConfig(yaml.safe_load("version: 1")) method_name = 'get_' + method real_method = getattr(atomic_reactor.plugins.pre_reactor_config, method_name) if fallback is True: output = real_method(workflow, fall_source.conf[method]) else: if fallback is False: output = real_method(workflow) else: with pytest.raises(KeyError): real_method(workflow) return expected = yaml.safe_load(REACTOR_CONFIG_MAP)[method] if method == 'registries': registries_cm = {} for registry in expected: reguri = RegistryURI(registry.get('url')) regdict = {} regdict['version'] = reguri.version if registry.get('auth'): regdict['secret'] = registry['auth']['cfg_path'] regdict['insecure'] = registry.get('insecure', False) regdict['expected_media_types'] = registry.get('expected_media_types', []) registries_cm[reguri.docker_uri] = regdict if fallback: output = real_method(workflow, registries_cm) assert output == registries_cm return if method == 'source_registry': expect = { 'uri': RegistryURI(expected['url']), 'insecure': expected.get('insecure', False) } if fallback: output = real_method(workflow, expect) assert output['insecure'] == expect['insecure'] assert output['uri'].uri == expect['uri'].uri return assert output == expected @pytest.mark.parametrize('fallback', (True, False)) @pytest.mark.parametrize(('config', 'expect'), [ ("""\ version: 1 platform_descriptors: - platform: x86_64 architecture: amd64 """, {'x86_64': 'amd64', 'ppc64le': 'ppc64le'}), ]) def test_get_platform_to_goarch_mapping(self, fallback, config, expect): _, workflow = self.prepare() workflow.plugin_workspace[ReactorConfigPlugin.key] = {} config_json = read_yaml(config, 'schemas/config.json') workspace = workflow.plugin_workspace[ReactorConfigPlugin.key] workspace[WORKSPACE_CONF_KEY] = ReactorConfig(config_json) kwargs = {} if fallback: kwargs['descriptors_fallback'] = {'x86_64': 'amd64'} platform_to_goarch = get_platform_to_goarch_mapping(workflow, **kwargs) goarch_to_platform = get_goarch_to_platform_mapping(workflow, **kwargs) for plat, goarch in expect.items(): assert platform_to_goarch[plat] == goarch assert goarch_to_platform[goarch] == plat @pytest.mark.parametrize(('config', 'expect'), [ ("""\ version: 1 default_image_build_method: buildah_bud """, "buildah_bud"), ("""\ version: 1 default_image_build_method: imagebuilder """, "imagebuilder"), ("""\ version: 1 """, CONTAINER_DEFAULT_BUILD_METHOD), ]) def test_get_default_image_build_method(self, config, expect): config_json = read_yaml(config, 'schemas/config.json') _, workflow = self.prepare() workspace = workflow.plugin_workspace.setdefault(ReactorConfigPlugin.key, {}) workspace[WORKSPACE_CONF_KEY] = ReactorConfig(config_json) method = get_default_image_build_method(workflow) assert method == expect @pytest.mark.parametrize(('config', 'expect'), [ ("""\ version: 1 buildstep_alias: docker_api: imagebuilder """, {'docker_api': 'imagebuilder'}), ("""\ version: 1 buildstep_alias: another: docker_api """, {'another': 'docker_api'}), ("""\ version: 1 buildstep_alias: docker_api: imagebuilder another: imagebuilder """, {'docker_api': 'imagebuilder', 'another': 'imagebuilder'}), ("""\ version: 1 """, {}), ]) def test_get_buildstep_alias(self, config, expect): config_json = read_yaml(config, 'schemas/config.json') _, workflow = self.prepare() workspace = workflow.plugin_workspace.setdefault(ReactorConfigPlugin.key, {}) workspace[WORKSPACE_CONF_KEY] = ReactorConfig(config_json) method = get_buildstep_alias(workflow) assert method == expect @pytest.mark.parametrize(('config', 'source_buildstep', 'expect_source', 'expect_default', 'will_raise'), [ ("""\ version: 1 default_image_build_method: docker_api buildstep_alias: docker_api: imagebuilder """, None, None, 'imagebuilder', False), ("""\ version: 1 buildstep_alias: docker_api: imagebuilder """, 'docker_api', 'imagebuilder', 'imagebuilder', False), ("""\ version: 1 default_image_build_method: buildah_bud """, None, None, None, True), ("""\ version: 1 default_image_build_method: docker_api """, 'buildah_bud', None, None, True), ]) def test_get_buildstep_alias_setting(self, tmpdir, config, source_buildstep, expect_source, expect_default, will_raise): filename = os.path.join(str(tmpdir), 'config.yaml') with open(filename, 'w') as fp: fp.write(dedent(config)) tasker, workflow = self.prepare() workflow.builder.source.config.image_build_method = source_buildstep plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir)) if will_raise: with pytest.raises(NotImplementedError): plugin.run() return assert plugin.run() is None assert workflow.builder.source.config.image_build_method == expect_source assert workflow.default_image_build_method == expect_default assert workflow.builder.tasker.build_method == expect_source or expect_default @pytest.mark.parametrize('fallback', (True, False)) @pytest.mark.parametrize(('config', 'expect'), [ ("""\ version: 1 build_image_override: ppc64le: registry.example.com/buildroot-ppc64le:latest arm: registry.example.com/buildroot-arm:latest """, {'ppc64le': 'registry.example.com/buildroot-ppc64le:latest', 'arm': 'registry.example.com/buildroot-arm:latest'}), ]) def test_get_build_image_override(self, fallback, config, expect): _, workflow = self.prepare() workflow.plugin_workspace[ReactorConfigPlugin.key] = {} config_json = read_yaml(config, 'schemas/config.json') workspace = workflow.plugin_workspace[ReactorConfigPlugin.key] workspace[WORKSPACE_CONF_KEY] = ReactorConfig(config_json) kwargs = {} if fallback: kwargs['fallback'] = expect build_image_override = get_build_image_override(workflow, **kwargs) assert build_image_override == expect @pytest.mark.parametrize(('config', 'fallback', 'expect'), [ ("""\ version: 1 flatpak: base_image: fedora:latest """, "x", "fedora:latest"), ("""\ version: 1 flatpak: {} """, "x", "x"), ("""\ version: 1 """, "x", "x"), ("""\ version: 1 """, None, None), ("""\ version: 1 flatpak: {} """, None, None), ]) def test_get_flatpak_base_image(self, config, fallback, expect): config_json = read_yaml(config, 'schemas/config.json') _, workflow = self.prepare() workflow.plugin_workspace[ReactorConfigPlugin.key] = { WORKSPACE_CONF_KEY: ReactorConfig(config_json) } kwargs = {} if fallback: kwargs['fallback'] = fallback if expect: base_image = get_flatpak_base_image(workflow, **kwargs) assert base_image == expect else: with pytest.raises(KeyError): get_flatpak_base_image(workflow, **kwargs) @pytest.mark.parametrize(('config', 'fallback', 'expect'), [ ("""\ version: 1 flatpak: metadata: labels """, "annotations", "labels"), ("""\ version: 1 flatpak: {} """, "annotations", "annotations"), ("""\ version: 1 """, "annotations", "annotations"), ("""\ version: 1 """, None, None), ("""\ version: 1 flatpak: {} """, None, None), ]) def test_get_flatpak_metadata(self, config, fallback, expect): config_json = read_yaml(config, 'schemas/config.json') _, workflow = self.prepare() workflow.plugin_workspace[ReactorConfigPlugin.key] = { WORKSPACE_CONF_KEY: ReactorConfig(config_json) } kwargs = {} if fallback: kwargs['fallback'] = fallback if expect: base_image = get_flatpak_metadata(workflow, **kwargs) assert base_image == expect else: with pytest.raises(KeyError): get_flatpak_metadata(workflow, **kwargs) @pytest.mark.parametrize('fallback', (True, False)) @pytest.mark.parametrize(('config', 'raise_error'), [ ("""\ version: 1 koji: hub_url: https://koji.example.com/hub root_url: https://koji.example.com/root auth: proxyuser: proxyuser krb_principal: krb_principal krb_keytab_path: /tmp/krb_keytab """, False), ("""\ version: 1 koji: hub_url: https://koji.example.com/hub root_url: https://koji.example.com/root auth: proxyuser: proxyuser krb_principal: krb_principal krb_keytab_path: /tmp/krb_keytab use_fast_upload: false """, False), ("""\ version: 1 koji: hub_url: https://koji.example.com/hub root_url: https://koji.example.com/root auth: proxyuser: proxyuser ssl_certs_dir: /var/certs """, False), ("""\ version: 1 koji: hub_url: https://koji.example.com/hub root_url: https://koji.example.com/root auth: proxyuser: proxyuser """, False), ("""\ version: 1 koji: hub_url: https://koji.example.com/hub root_url: https://koji.example.com/root auth: """, True), ("""\ version: 1 koji: hub_url: https://koji.example.com/hub root_url: https://koji.example.com/root auth: proxyuser: proxyuser krb_principal: krb_principal krb_keytab_path: /tmp/krb_keytab ssl_certs_dir: /var/certs """, True), ("""\ version: 1 koji: hub_url: https://koji.example.com/hub root_url: https://koji.example.com/root auth: proxyuser: proxyuser krb_keytab_path: /tmp/krb_keytab """, True), ("""\ version: 1 koji: hub_url: https://koji.example.com/hub root_url: https://koji.example.com/root auth: proxyuser: proxyuser krb_principal: krb_principal """, True), ("""\ version: 1 koji: hub_url: https://koji.example.com/hub root_url: https://koji.example.com/root auth: proxyuser: proxyuser krb_principal: krb_principal ssl_certs_dir: /var/certs """, True), ("""\ version: 1 koji: hub_url: https://koji.example.com/hub root_url: https://koji.example.com/root auth: proxyuser: proxyuser krb_keytab_path: /tmp/krb_keytab ssl_certs_dir: /var/certs """, True), ]) def test_get_koji_session(self, fallback, config, raise_error): _, workflow = self.prepare() workflow.plugin_workspace[ReactorConfigPlugin.key] = {} if raise_error: with pytest.raises(Exception): read_yaml(config, 'schemas/config.json') return config_json = read_yaml(config, 'schemas/config.json') auth_info = { "proxyuser": config_json['koji']['auth'].get('proxyuser'), "ssl_certs_dir": config_json['koji']['auth'].get('ssl_certs_dir'), "krb_principal": config_json['koji']['auth'].get('krb_principal'), "krb_keytab": config_json['koji']['auth'].get('krb_keytab_path') } use_fast_upload = config_json['koji'].get('use_fast_upload', True) fallback_map = {} if fallback: fallback_map = {'auth': deepcopy(auth_info), 'hub_url': config_json['koji']['hub_url']} fallback_map['auth']['krb_keytab_path'] = fallback_map['auth'].pop('krb_keytab') fallback_map['use_fast_upload'] = use_fast_upload else: workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] = \ ReactorConfig(config_json) (flexmock(atomic_reactor.utils.koji) .should_receive('create_koji_session') .with_args(config_json['koji']['hub_url'], auth_info, use_fast_upload) .once() .and_return(True)) get_koji_session(workflow, fallback_map) @pytest.mark.parametrize('fallback', (True, False)) @pytest.mark.parametrize('root_url', ( 'https://koji.example.com/root', 'https://koji.example.com/root/', None )) def test_get_koji_path_info(self, fallback, root_url): _, workflow = self.prepare() workflow.plugin_workspace[ReactorConfigPlugin.key] = {} config = { 'version': 1, 'koji': { 'hub_url': 'https://koji.example.com/hub', 'auth': { 'ssl_certs_dir': '/var/certs' } } } expected_root_url = 'https://koji.example.com/root' if root_url: config['koji']['root_url'] = root_url config_yaml = yaml.safe_dump(config) expect_error = not root_url if expect_error: with pytest.raises(Exception): read_yaml(config_yaml, 'schemas/config.json') return parsed_config = read_yaml(config_yaml, 'schemas/config.json') fallback_map = {} if fallback: fallback_map = deepcopy(config['koji']) else: workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] = \ ReactorConfig(parsed_config) (flexmock(koji.PathInfo) .should_receive('__init__') .with_args(topdir=expected_root_url) .once()) get_koji_path_info(workflow, fallback_map) @pytest.mark.parametrize('fallback', (True, False)) @pytest.mark.parametrize(('config', 'raise_error'), [ ("""\ version: 1 odcs: api_url: https://odcs.example.com/api/1 auth: ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret signing_intents: - name: release keys: [R123] default_signing_intent: default """, False), ("""\ version: 1 odcs: api_url: https://odcs.example.com/api/1 auth: ssl_certs_dir: nonexistent signing_intents: - name: release keys: [R123] default_signing_intent: default """, False), ("""\ version: 1 odcs: api_url: https://odcs.example.com/api/1 auth: openidc_dir: /var/run/open_idc signing_intents: - name: release keys: [R123] default_signing_intent: default """, False), ("""\ version: 1 odcs: api_url: https://odcs.example.com/api/1 auth: openidc_dir: /var/run/open_idc ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret signing_intents: - name: release keys: [R123] default_signing_intent: default """, True), ("""\ version: 1 odcs: api_url: https://odcs.example.com/api/1 auth: openidc_dir: /var/run/open_idc signing_intents: - name: release keys: [R123] """, True), ("""\ version: 1 odcs: api_url: https://odcs.example.com/api/1 auth: openidc_dir: /var/run/open_idc default_signing_intent: default """, True), ("""\ version: 1 odcs: auth: openidc_dir: /var/run/open_idc signing_intents: - name: release keys: [R123] default_signing_intent: default """, True), ]) def test_get_odcs_session(self, tmpdir, fallback, config, raise_error): _, workflow = self.prepare() workflow.plugin_workspace[ReactorConfigPlugin.key] = {} if raise_error: with pytest.raises(Exception): read_yaml(config, 'schemas/config.json') return config_json = read_yaml(config, 'schemas/config.json') auth_info = {'insecure': config_json['odcs'].get('insecure', False)} if 'openidc_dir' in config_json['odcs']['auth']: config_json['odcs']['auth']['openidc_dir'] = str(tmpdir) filename = str(tmpdir.join('token')) with open(filename, 'w') as fp: fp.write("my_token") auth_info['token'] = "<PASSWORD>" ssl_dir_raise = False if 'ssl_certs_dir' in config_json['odcs']['auth']: if config_json['odcs']['auth']['ssl_certs_dir'] != "nonexistent": config_json['odcs']['auth']['ssl_certs_dir'] = str(tmpdir) filename = str(tmpdir.join('cert')) with open(filename, 'w') as fp: fp.write("my_cert") auth_info['cert'] = filename else: ssl_dir_raise = True fallback_map = {} if fallback: fallback_map = {'auth': deepcopy(auth_info), 'api_url': config_json['odcs']['api_url']} fallback_map['auth']['ssl_certs_dir'] = config_json['odcs']['auth'].get('ssl_certs_dir') fallback_map['auth']['openidc_dir'] = config_json['odcs']['auth'].get('openidc_dir') else: workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] =\ ReactorConfig(config_json) if not ssl_dir_raise: (flexmock(atomic_reactor.utils.odcs.ODCSClient) .should_receive('__init__') .with_args(config_json['odcs']['api_url'], **auth_info) .once() .and_return(None)) get_odcs_session(workflow, fallback_map) else: with pytest.raises(KeyError): get_odcs_session(workflow, fallback_map) @pytest.mark.parametrize('fallback', (True, False)) @pytest.mark.parametrize(('config', 'raise_error'), [ ("""\ version: 1 smtp: host: smtp.example.com from_address: <EMAIL> """, False), ("""\ version: 1 smtp: from_address: <EMAIL> """, True), ("""\ version: 1 smtp: host: smtp.example.com """, True), ("""\ version: 1 smtp: """, True), ]) def test_get_smtp_session(self, fallback, config, raise_error): _, workflow = self.prepare() workflow.plugin_workspace[ReactorConfigPlugin.key] = {} if raise_error: with pytest.raises(Exception): read_yaml(config, 'schemas/config.json') return config_json = read_yaml(config, 'schemas/config.json') fallback_map = {} if fallback: fallback_map['host'] = config_json['smtp']['host'] else: workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] =\ ReactorConfig(config_json) (flexmock(smtplib.SMTP) .should_receive('__init__') .with_args(config_json['smtp']['host']) .once() .and_return(None)) get_smtp_session(workflow, fallback_map) @pytest.mark.parametrize(('config', 'error'), [ ("""\ version: 1 cachito: api_url: https://cachito.example.com auth: ssl_certs_dir: /var/run/secrets/atomic-reactor/cachitosecret """, False), ("""\ version: 1 cachito: api_url: https://cachito.example.com insecure: true auth: ssl_certs_dir: /var/run/secrets/atomic-reactor/cachitosecret """, False), ("""\ version: 1 cachito: api_url: https://cachito.example.com auth: """, ValidationError), ("""\ version: 1 cachito: api_url: https://cachito.example.com """, ValidationError), ("""\ version: 1 cachito: auth: ssl_certs_dir: /var/run/secrets/atomic-reactor/cachitosecret """, ValidationError), ("""\ version: 1 cachito: api_url: https://cachito.example.com auth: ssl_certs_dir: /var/run/secrets/atomic-reactor/cachitosecret spam: ham """, ValidationError), ("""\ version: 1 cachito: api_url: https://cachito.example.com auth: ssl_certs_dir: nonexistent """, False), ]) def test_get_cachito_session(self, tmpdir, config, error): _, workflow = self.prepare() workflow.plugin_workspace[ReactorConfigPlugin.key] = {} if error: with pytest.raises(error): read_yaml(config, 'schemas/config.json') return config_json = read_yaml(config, 'schemas/config.json') auth_info = {'insecure': config_json['cachito'].get('insecure', False)} ssl_dir_raise = False if 'ssl_certs_dir' in config_json['cachito']['auth']: if config_json['cachito']['auth']['ssl_certs_dir'] != "nonexistent": config_json['cachito']['auth']['ssl_certs_dir'] = str(tmpdir) filename = str(tmpdir.join('cert')) with open(filename, 'w') as fp: fp.write("my_cert") auth_info['cert'] = filename else: ssl_dir_raise = True workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] =\ ReactorConfig(config_json) if not ssl_dir_raise: (flexmock(atomic_reactor.utils.cachito.CachitoAPI) .should_receive('__init__') .with_args(config_json['cachito']['api_url'], **auth_info) .once() .and_return(None)) get_cachito_session(workflow) else: with pytest.raises(RuntimeError, match="Cachito ssl_certs_dir doesn't exist"): get_cachito_session(workflow) @pytest.mark.parametrize('fallback', (True, False)) @pytest.mark.parametrize('build_json_dir', [ None, "/tmp/build_json_dir", ]) @pytest.mark.parametrize(('config', 'raise_error'), [ ("""\ version: 1 openshift: url: https://openshift.example.com auth: ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret """, False), ("""\ version: 1 openshift: url: https://openshift.example.com """, False), ("""\ version: 1 openshift: url: https://openshift.example.com auth: krb_principal: principal krb_keytab_path: /var/keytab """, False), ("""\ version: 1 openshift: url: https://openshift.example.com auth: krb_principal: principal krb_keytab_path: /var/keytab krb_cache_path: /var/krb/cache """, False), ("""\ version: 1 openshift: url: https://openshift.example.com auth: enable: True """, False), ("""\ version: 1 openshift: url: https://openshift.example.com auth: krb_keytab_path: /var/keytab """, True), ("""\ version: 1 openshift: url: https://openshift.example.com auth: krb_principal: principal """, True), ("""\ version: 1 openshift: auth: ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret """, True), ("""\ version: 1 openshift: auth: krb_principal: principal krb_keytab_path: /var/keytab """, True), ("""\ version: 1 openshift: url: https://openshift.example.com auth: """, True), ("""\ version: 1 openshift: auth: ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret """, True), ]) def test_get_openshift_session(self, fallback, build_json_dir, config, raise_error): _, workflow = self.prepare() workflow.plugin_workspace[ReactorConfigPlugin.key] = {} if build_json_dir: config += " build_json_dir: " + build_json_dir if raise_error: with pytest.raises(Exception): read_yaml(config, 'schemas/config.json') return config_json = read_yaml(config, 'schemas/config.json') auth_info = { 'openshift_url': config_json['openshift']['url'], 'verify_ssl': not config_json['openshift'].get('insecure', False), 'use_auth': False, 'conf_file': None, 'namespace': 'namespace', 'build_json_dir': build_json_dir } if config_json['openshift'].get('auth'): if config_json['openshift']['auth'].get('krb_keytab_path'): auth_info['kerberos_keytab'] =\ config_json['openshift']['auth'].get('krb_keytab_path') if config_json['openshift']['auth'].get('krb_principal'): auth_info['kerberos_principal'] =\ config_json['openshift']['auth'].get('krb_principal') if config_json['openshift']['auth'].get('krb_cache_path'): auth_info['kerberos_ccache'] =\ config_json['openshift']['auth'].get('krb_cache_path') if config_json['openshift']['auth'].get('ssl_certs_dir'): auth_info['client_cert'] =\ os.path.join(config_json['openshift']['auth'].get('ssl_certs_dir'), 'cert') auth_info['client_key'] =\ os.path.join(config_json['openshift']['auth'].get('ssl_certs_dir'), 'key') auth_info['use_auth'] = config_json['openshift']['auth'].get('enable', False) fallback_map = {} if fallback: fallback_map = {'url': config_json['openshift']['url'], 'insecure': config_json['openshift'].get('insecure', False), 'build_json_dir': build_json_dir} if config_json['openshift'].get('auth'): fallback_map['auth'] = {} fallback_map['auth']['krb_keytab_path'] =\ config_json['openshift']['auth'].get('krb_keytab_path') fallback_map['auth']['krb_principal'] =\ config_json['openshift']['auth'].get('krb_principal') fallback_map['auth']['enable'] =\ config_json['openshift']['auth'].get('enable', False) fallback_map['auth']['krb_cache_path'] =\ config_json['openshift']['auth'].get('krb_cache_path') fallback_map['auth']['ssl_certs_dir'] =\ config_json['openshift']['auth'].get('ssl_certs_dir') else: workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] =\ ReactorConfig(config_json) (flexmock(osbs.conf.Configuration) .should_call('__init__') .with_args(**auth_info) .once()) (flexmock(osbs.api.OSBS) .should_call('__init__') .once()) flexmock(os, environ={'BUILD': '{"metadata": {"namespace": "namespace"}}'}) get_openshift_session(workflow, fallback_map) @pytest.mark.parametrize('config, valid', [ ("""\ version: 1 operator_manifests: allowed_registries: null """, True), # minimal valid example, allows all registries ("""\ version: 1 operator_manifests: allowed_registries: - foo - bar repo_replacements: - registry: foo package_mappings_url: https://somewhere.net/mapping.yaml registry_post_replace: - old: foo new: bar """, True), # all known properties ("""\ version: 1 operator_manifests: null """, False), # has to be a dict ("""\ version: 1 operator_manifests: {} """, False), # allowed_registries is required ("""\ version: 1 operator_manifests: allowed_registries: [] """, False), # if not null, allowed_registries must not be empty ("""\ version: 1 operator_manifests: allowed_registries: null something_else: null """, False), # additional properties not allowed ("""\ version: 1 operator_manifests: allowed_registries: null registry_post_replace: - old: foo """, False), # missing replacement registry ("""\ version: 1 operator_manifests: allowed_registries: null registry_post_replace: - new: foo """, False), # missing original registry ("""\ version: 1 operator_manifests: allowed_registries: null repo_replacements: - registry: foo """, False), # missing package mappings url ("""\ version: 1 operator_manifests: allowed_registries: null repo_replacements: - package_mappings_url: https://somewhere.net/mapping.yaml """, False), # missing registry ("""\ version: 1 operator_manifests: allowed_registries: null, repo_replacements: - registry: foo package_mappings_url: mapping.yaml """, False), # package mappings url is not a url ]) def test_get_operator_manifests(self, config, valid): _, workflow = self.prepare() if valid: config_json = read_yaml(config, 'schemas/config.json') else: with pytest.raises(ValidationError): read_yaml(config, 'schemas/config.json') return reactor_config_results = workflow.plugin_workspace.setdefault(ReactorConfigPlugin.key, {}) reactor_config_results[WORKSPACE_CONF_KEY] = ReactorConfig(config_json) operator_config = get_operator_manifests(workflow) assert isinstance(operator_config, dict) assert "allowed_registries" in operator_config
<reponame>nathanjenx/cairis # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from cairis.core.armid import * import wx from cairis.core.Borg import Borg from cairis.core.VulnerabilityParameters import VulnerabilityParameters __author__ = '<NAME>' class TraceExplorer(wx.Dialog): def __init__(self,parent,traceDimension,isFrom,envName=''): wx.Dialog.__init__(self,parent,TRACE_ID,'Contributions',style=wx.DEFAULT_DIALOG_STYLE|wx.MAXIMIZE_BOX|wx.THICK_FRAME|wx.RESIZE_BORDER,size=(800,300)) b = Borg() self.dbProxy = b.dbProxy self.theTraceDimension = traceDimension self.isFromIndicator = isFrom self.theSelectedValue = '' self.theToDimension = '' self.theLabel = '' self.theEnvironmentName = envName mainSizer = wx.BoxSizer(wx.VERTICAL) frameSizer = wx.BoxSizer(wx.HORIZONTAL) mainSizer.Add(frameSizer,1,wx.EXPAND) dimensionSizer = wx.BoxSizer(wx.VERTICAL) frameSizer.Add(dimensionSizer,0,wx.EXPAND) dimensionLabel = wx.StaticText(self,-1,'Dimension:') dimensionSizer.Add(dimensionLabel) dimensions = self.dbProxy.getTraceDimensions(self.theTraceDimension,self.isFromIndicator) self.dimensionList = wx.ListBox(self,TRACE_LISTDIM_ID,choices=dimensions,style=wx.LB_SINGLE) dimensionSizer.Add(self.dimensionList,1,wx.EXPAND) valueSizer = wx.BoxSizer(wx.VERTICAL) frameSizer.Add(valueSizer,1,wx.EXPAND) valueLabel = wx.StaticText(self,-1,'Value:') valueSizer.Add(valueLabel) self.valueList = wx.ListBox(self,TRACE_LISTVALUES_ID,style=wx.LB_SINGLE) valueSizer.Add(self.valueList,1,wx.EXPAND) labelBox = wx.StaticBox(self,-1,'Label') labelBoxSizer = wx.StaticBoxSizer(labelBox,wx.HORIZONTAL) mainSizer.Add(labelBoxSizer,0,wx.EXPAND) self.labelCtrl = wx.TextCtrl(self,TRACE_TEXTLABEL_ID,'') labelBoxSizer.Add(self.labelCtrl,1,wx.EXPAND) self.labelCtrl.Disable() buttonSizer = wx.BoxSizer(wx.HORIZONTAL) mainSizer.Add(buttonSizer,0,wx.ALIGN_CENTER) addButton = wx.Button(self,TRACE_BUTTONADD_ID,"Add") buttonSizer.Add(addButton) cancelButton = wx.Button(self,wx.ID_CANCEL,"Cancel") buttonSizer.Add(cancelButton) self.SetSizer(mainSizer) wx.EVT_LISTBOX(self.dimensionList,TRACE_LISTDIM_ID,self.onDimItemSelected) wx.EVT_LISTBOX(self.valueList,TRACE_LISTVALUES_ID,self.onValueItemSelected) wx.EVT_BUTTON(self,TRACE_BUTTONADD_ID,self.onAdd) wx.EVT_BUTTON(self,wx.ID_CANCEL,self.onClose) def onDimItemSelected(self,evt): valueIdx = self.valueList.GetSelection() self.valueList.Deselect(valueIdx) self.theToDimension = self.dimensionList.GetStringSelection() if (self.theToDimension == 'requirement'): self.labelCtrl.Enable() else: self.labelCtrl.Disable() if (self.theToDimension): dimensionValues = self.dbProxy.getDimensionNames(self.theToDimension,self.theEnvironmentName) self.valueList.Set(dimensionValues) def onValueItemSelected(self,evt): self.theSelectedValue = self.valueList.GetStringSelection() def toDimension(self): return self.theToDimension def fromDimension(self): if (self.isFromIndicator == True): return self.toDimension() def toValue(self): return self.theSelectedValue def label(self): return self.labelCtrl.GetValue() def toId(self): return self.dbProxy.getDimensionId(self.theSelectedValue,self.theToDimension) def onAdd(self,evt): if len(self.theToDimension) == 0: dlg = wx.MessageDialog(self,'No target dimension has been selected','Add trace',wx.OK) dlg.ShowModal() dlg.Destroy() return elif (len(self.theSelectedValue) == 0): dlg = wx.MessageDialog(self,'No dimension value has been selected','Add trace',wx.OK) dlg.ShowModal() dlg.Destroy() return else: idx = self.dimensionList.GetSelection() self.dimensionList.Deselect(idx) idx = self.valueList.GetSelection() self.valueList.Deselect(idx) self.EndModal(TRACE_BUTTONADD_ID) def onClose(self,evt): idx = self.dimensionList.GetSelection() self.dimensionList.Deselect(idx) self.EndModal(wx.ID_CLOSE)
<reponame>coll-gate/collgate # -*- coding: utf-8; -*- # # @file descriptorstypes.py # @brief Setup the types of descriptors. # @author <NAME> (INRA UMR1095) # @date 2016-09-01 # @copyright Copyright (c) 2016 INRA/CIRAD # @license MIT (see LICENSE file) # @details DESCRIPTORS = { 'biological_status': { 'id': None, 'name': 'biological_status', 'code': 'MCPD_SAMPSTAT', 'group_name': 'MCPD', 'label': {'en': 'Biological status', 'fr': 'Status biologique'}, 'can_delete': False, 'can_modify': False, 'description': 'Biological status of an accession', 'format': { 'type': 'enum_pair', 'fields': ['classification', 'value'], 'trans': True, 'list_type': 'dropdown', 'display_fields': 'hier0-value1', 'sortby_field': 'value0' } }, 'acquisition_date': { 'id': None, 'name': 'acquisition_date', 'code': 'MCPD_ACQDATE', 'group_name': 'MCPD', 'label': {'en': 'Acquisition date', 'fr': "Date d'acquisition"}, 'can_delete': False, 'can_modify': False, 'description': 'Date on which the accession entered the collection', 'format': { 'type': 'imprecise_date' }, '__comment': 'Not necessary to declare a specific descriptor type. Can use a generic type defined in common group. Need to choose if this is really a date or just a 4 characters string for the wheat sample data' }, 'pedigree': { 'id': None, 'name': 'pedigree', 'code': 'MCPD_ANCEST', 'group_name': 'MCPD', 'label': {'en': 'Pedigree', 'fr': 'Pédigré'}, 'can_delete': False, 'can_modify': False, 'description': 'Pedigree of an accession as free text, string with a maximum of 4000 characters', 'format': { 'type': 'string', 'regexp': '^.{0,4000}$' }, '__comment': 'Not necessary to declare a specific descriptor type. Can use a generic type defined in common group.' }, 'country_of_origin': { 'id': None, 'name': 'country_of_origin', 'code': 'MCPD_ORIGCTY', 'group_name': 'MCPD', 'label': {'en': 'Origin country', 'fr': "Pays d'origine"}, 'can_delete': False, 'can_modify': False, 'description': 'Country of origin of an accession', 'format': { 'type': 'country' }, '__comment': 'Not necessary to declare a specific descriptor type. Can use a generic type defined in common group.' }, 'donor_institution_name': { 'id': None, 'name': 'donor_institution_name', 'code': 'MCPD_DONORNAME', 'group_name': 'MCPD', 'label': {'en': 'Donnor name', 'fr': 'Nom du donateur'}, 'can_delete': False, 'can_modify': False, 'description': 'Name of the donor institution of an accession, string with a maximum of 255 characters', 'format': { 'type': 'string', 'regexp': '^.{0,255}$' }, '__comment': 'Not necessary to declare a specific descriptor type. Can use a generic type defined in common group.' }, # 'donor_institution_name': { # 'id': None, # 'name': 'donor_institution_name', # 'code': 'MCPD_DONORNAME', # 'group_name': 'MCPD', # 'label': {'en': 'Donnor name', 'fr': 'Nom du donateur'}, # 'can_delete': False, # 'can_modify': False, # 'description': 'Name of the donor institution of an accession, organisation establishment', # 'format': { # 'type': 'entity', # 'model': 'organisation.establishment' # }, # '__comment': 'Not necessary to declare a specific descriptor type. Can use a generic type defined in common group.' # }, 'donor_accession_number': { 'id': None, 'name': 'donor_accession_number', 'code': 'MCPD_DONORNNUMB', 'group_name': 'MCPD', 'label': {'en': 'Donnor accession number', 'fr': 'Numéro d\'accession du donateur'}, 'can_delete': False, 'can_modify': False, 'description': 'Identifier assigned to an accession by the donor', 'format': { 'type': 'string', 'regexp': '^.{0,255}$' }, '__comment': 'Not necessary to declare a specific descriptor type. Can use a generic type defined in common group.' }, 'batch_creation_date': { 'id': None, 'name': 'batch_creation_date', 'code': 'BATCH_CREATDATE', 'group_name': 'BATCH', 'label': {'en': 'Creation date', 'fr': "Date de création"}, 'can_delete': False, 'can_modify': False, 'description': 'Date on which the batch has been created', 'format': { 'type': 'imprecise_date' }, '__comment': 'Not necessary to declare a specific descriptor type. Can use a generic type defined in common group.' }, 'batch_destruction_date': { 'id': None, 'name': 'batch_destruction_date', 'code': 'BATCH_DESTRDATE', 'group_name': 'BATCH', 'label': {'en': 'Destruction date', 'fr': "Date de destruction"}, 'can_delete': False, 'can_modify': False, 'description': 'Date on which the batch has been destroyed', 'format': { 'type': 'imprecise_date' }, '__comment': 'Not necessary to declare a specific descriptor type. Can use a generic type defined in common group.' }, 'batch_comment': { 'id': None, 'name': 'batch_comment', 'code': 'BATCH_COMMENT', 'group_name': 'BATCH', 'label': {'en': 'Comment', 'fr': "Commentaire"}, 'can_delete': False, 'can_modify': False, 'description': 'Comment', 'format': { 'type': 'string' }, '__comment': 'Not necessary to declare a specific descriptor type. Can use a generic type defined in common group.' } } def fixture(fixture_manager, factory_manager): fixture_manager.create_or_update_descriptors(DESCRIPTORS)
<filename>src/speech/train_ConvLSTM.py import torch from torch import optim from raw_audio_model import RawAudioModel from ConvLSTM import ConvLSTM from process_raw_audio_model import IEMOCAP, my_collate_train, my_collate_test from torch.utils.data import DataLoader from torch.optim.lr_scheduler import ReduceLROnPlateau from torch.optim.lr_scheduler import CosineAnnealingLR import pdb from torch.nn import DataParallel import pickle import numpy as np path="/scratch/speech/models/classification/ConvLSTM_data_debug.pickle" device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') hidden_channels=[64,32,16] kernel_size=[9,5,5] step=100 model = ConvLSTM(1, hidden_channels,kernel_size,step,True) print("============================ Number of parameters ====================================") print(str(sum(p.numel() for p in model.parameters() if p.requires_grad))) model.cuda() device_ids=[0,1] num_devices=len(device_ids) model=DataParallel(model,device_ids=device_ids) model.train() # Use Adam as the optimizer with learning rate 0.01 to make it fast for testing purposes optimizer = optim.Adam(model.parameters(), lr=0.001) optimizer2=optim.SGD(model.parameters(), lr=0.1) scheduler = ReduceLROnPlateau(optimizer=optimizer,factor=0.5, patience=2, threshold=1e-3) #scheduler2=ReduceLROnPlateau(optimizer=optimizer2, factor=0.5, patience=2, threshold=1e-3) scheduler2 =CosineAnnealingLR(optimizer2, T_max=300, eta_min=0.0001) # Load the training data training_data = IEMOCAP(train=True, segment=True) train_loader = DataLoader(dataset=training_data, batch_size=200, shuffle=True, collate_fn=my_collate_train, num_workers=0, drop_last=True) testing_data = IEMOCAP(train=False, segment=True) test_loader = DataLoader(dataset=testing_data, batch_size=100, shuffle=True, collate_fn=my_collate_test, num_workers=0) print("=================") print(len(training_data)) print("===================") test_acc=[] train_acc=[] test_loss=[] train_loss=[] epoch=0 torch.save(model.module.state_dict(), "/scratch/speech/models/classification/ConvLSTM_checkpoint_epoch_{}.pt".format(epoch)) for epoch in range(300): # again, normally you would NOT do 300 epochs, it is toy data print("===================================" + str(epoch+1) + "==============================================") losses = 0 correct=0 losses_test = 0 correct_test = 0 model.train() for j, (input, target, _) in enumerate(train_loader): if (j+1)%5==0: print("================================= Batch"+ str(j+1)+ "===================================================") input=input.float() input = input.unsqueeze(1) input=torch.split(input,int(32000/step),dim=2) model.zero_grad() out, loss = model(input, target) #pdb.set_trace() loss = torch.mean(loss,dim=0) out=torch.flatten(out,start_dim=0,end_dim=1) #pdb.set_trace() losses += loss.item() * target.shape[0] loss.backward() optimizer.step() index = torch.argmax(out, dim=1) target_index = torch.argmax(target, dim=1).to(device) correct += sum(index == target_index).item() accuracy=correct*1.0/(len(training_data)) losses=losses / (len(training_data)) # we save the model, and run it on single gpu torch.save(model.module.state_dict(), "/scratch/speech/models/classification/ConvLSTM_checkpoint_epoch_{}.pt".format(epoch+1)) model1=ConvLSTM(1, hidden_channels,kernel_size,step,True) model1.load_state_dict(torch.load("/scratch/speech/models/classification/ConvLSTM_checkpoint_epoch_{}.pt".format(epoch+1))) model1.eval() with torch.cuda.device(0): model1.cuda() with torch.no_grad(): for test_case, target, seq_length in test_loader: temp=[] for i in test_case: for k in i: temp.append(k) test_case=torch.from_numpy(np.array([i for i in temp])).to(device) length=test_case.shape[0] test_case=test_case.float() test_case = test_case.unsqueeze(1) test_case=torch.split(test_case,int(32000/step),dim=2) out,_ = model1(test_case, target, False) target_index = torch.argmax(target, dim=1).to(device) temp=0 temp1=0 for i,j in enumerate(target_index): temp1+=seq_length[i].item() if j==torch.argmax(torch.sum(out[temp:temp1,:],dim=0)): correct_test+=1 temp=temp1 accuracy_test = correct_test * 1.0 / (len(testing_data)) #if losses_test<0.95: scheduler=scheduler2; optimizer=optimizer2 # data gathering test_acc.append(accuracy_test) train_acc.append(accuracy) train_loss.append(losses) print("Epoch: {}-----------Training Loss: {} -------- Training Acc: {} -------- Testing Acc: {}".format(epoch+1,losses, accuracy, accuracy_test)+"\n") with open("/scratch/speech/models/classification/ConvLSTM_checkpoint_stats.txt","a+") as f: if epoch==0: f.write("+\n"+"============================= Begining New Model ======================================================="+"\n") f.write("Epoch: {}-----------Training Loss: {} -------- Training Acc: {} -------- Testing Acc: {}".format(epoch+1,losses, accuracy, accuracy_test)+"\n") scheduler.step(losses) #scheduler2.step() pickle_out=open("/scratch/speech/models/classification/ConvLSTM_checkpoint_stats.pkl","wb") pickle.dump({"test_acc":test_acc, "train_acc": train_acc, "train_loss": train_loss},pickle_out) pickle_out.close()
<gh_stars>0 # -*- coding: UTF-8 -*- ''' Created on 31 December 2014 @author: <NAME> Written By: <NAME> @Email: < robert [--DOT--] pastor0691 (--AT--) orange [--DOT--] fr > @http://trajectoire-predict.monsite-orange.fr/ @copyright: Copyright 2015 <NAME> This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. Manage the ground run phase ''' import math from trajectory.aerocalc.airspeed import tas2cas from trajectory.Environment.RunWayFile import RunWay from trajectory.Guidance.WayPointFile import Airport, WayPoint from trajectory.Guidance.GraphFile import Graph from trajectory.BadaAircraftPerformance.BadaAircraftFile import BadaAircraft from trajectory.Environment.Constants import Meter2Feet, Knots2MetersPerSecond , MeterPerSecond2Knots class GroundRunLeg(Graph): ''' ground run inputs are: 1) airport field elevation above sea level (meters) 2) runway true heading (in degrees) departure Ground Run : 1) initial speed is 0.0 meters / second arrival Ground Run: 1) final speed = taxi speed ''' aircraft = None airport = None runway = None elapsedTimeSeconds = 0.0 def __init__(self, runway, aircraft, airport): ''' base class init ''' Graph.__init__(self) self.className = self.__class__.__name__ assert (isinstance(runway, RunWay) and not(runway is None)) self.runway = runway print ( self.className + ': ground run - run-way true heading= ' + str(self.runway.getTrueHeadingDegrees()) + ' degrees' ) assert (isinstance(aircraft, BadaAircraft) and not(aircraft is None)) self.aircraft = aircraft assert (isinstance(airport, Airport) and not(airport is None)) self.airport = airport def computeTouchDownWayPoint(self): ''' get landing length in meters ''' landingLengthMeters = self.aircraft.getLandingLengthMeters() ''' run-way orientation ''' runwayTrueHeadingDegrees = self.runway.getTrueHeadingDegrees() ''' run-way end point ''' strRunWayEndPointName = self.airport.getName() + '-' + 'Rwy'+'-'+self.runway.getName() runWayEndPoint = WayPoint (Name = strRunWayEndPointName, LatitudeDegrees = self.runway.getLatitudeDegrees(), LongitudeDegrees = self.runway.getLongitudeDegrees(), AltitudeMeanSeaLevelMeters =self.airport.getFieldElevationAboveSeaLevelMeters()) strTouchDownWayPointName = self.airport.getName() + '-' + 'Rwy'+'-' + self.runway.getName() + '-' + 'touch-down' touchDownWayPoint = runWayEndPoint.getWayPointAtDistanceBearing(Name = strTouchDownWayPointName, DistanceMeters = landingLengthMeters, BearingDegrees = runwayTrueHeadingDegrees) touchDownWayPoint.setAltitudeMeanSeaLevelMeters(self.airport.getFieldElevationAboveSeaLevelMeters()) return touchDownWayPoint def buildArrivalGroundRun(self, deltaTimeSeconds, elapsedTimeSeconds, initialWayPoint): assert isinstance(initialWayPoint, WayPoint) ''' speed decreases from 1.2 V Stall to taxi speed (according to the airport elevation stall speed changes with air density) ''' ''' delta time in seconds ''' elapsedTimeSeconds = elapsedTimeSeconds ''' get landing length in meters ''' landingLengthMeters = self.aircraft.getLandingLengthMeters() ''' run-way orientation ''' runwayTrueHeadingDegrees = self.runway.getTrueHeadingDegrees() runwayTrueHeadingDegrees = math.fmod(runwayTrueHeadingDegrees + 180.0, 360.0) ''' run-way end point ''' strRunWayEndPointName = 'touch-down-rwy-'+ self.runway.getName() + '-' + self.airport.getName() ''' rename the initial way point ''' initialWayPoint.setName(strRunWayEndPointName) ''' graph index ''' index = 0 distanceStillToFlyMeters = landingLengthMeters ''' loop until => end of simulation ( aircraft' speed reduced to the taxi speed = 15 knots) ''' endOfSimulation = False while (endOfSimulation == False) : ''' initialisation ''' if index == 0: intermediateWayPoint = initialWayPoint ''' fly => decrease the true air speed ''' endOfSimulation, deltaDistanceMeters , altitudeMeters = self.aircraft.fly( elapsedTimeSeconds = elapsedTimeSeconds, deltaTimeSeconds = deltaTimeSeconds , distanceStillToFlyMeters = distanceStillToFlyMeters, currentPosition = intermediateWayPoint, distanceToLastFixMeters = 0.0) distanceStillToFlyMeters -= deltaDistanceMeters #trueAirSpeedMetersSecond = self.aircraft.getCurrentTrueAirSpeedMetersSecond() #print 'true air speed= ' + str(trueAirSpeedMetersSecond) + ' meters/second' ''' name of the next point ''' Name = '' if index == 0: Name = 'ground-run-pt-{0}-{1:.2f}-meters'.format(index-1, deltaDistanceMeters) #bearingDegrees = math.fmod ( runwayTrueHeadingDegrees + 180.0 , 360.0 ) bearingDegrees = runwayTrueHeadingDegrees newIntermediateWayPoint = intermediateWayPoint.getWayPointAtDistanceBearing(Name = Name, DistanceMeters = deltaDistanceMeters, BearingDegrees = bearingDegrees) ''' during the ground run - altitude = airport field elevation ''' newIntermediateWayPoint.setAltitudeMeanSeaLevelMeters(self.airport.getFieldElevationAboveSeaLevelMeters()) ''' update route way-point ''' elapsedTimeSeconds += deltaTimeSeconds newIntermediateWayPoint.setElapsedTimeSeconds(elapsedTimeSeconds) self.elapsedTimeSeconds = elapsedTimeSeconds ''' insert in the route ''' self.addVertex(newIntermediateWayPoint) ''' copy the intermediate way-point ''' intermediateWayPoint = newIntermediateWayPoint ''' increment the index ''' index += 1 #print '============ end of arrival ground run ======================' strRunWayEndPointName = self.airport.getName() + '-' + 'rwy'+'-' + self.runway.getName() intermediateWayPoint.setName(Name = strRunWayEndPointName) def buildDepartureGroundRun(self, deltaTimeSeconds, elapsedTimeSeconds, distanceStillToFlyMeters, distanceToLastFixMeters): ''' build the departure ground run ''' ''' elapsedTimeSeconds in seconds ''' elapsedTimeSeconds = elapsedTimeSeconds ''' run-way end point ''' strRunWayEndPointName = self.airport.getName() + '-' + 'Rwy'+'-'+self.runway.getName() runWayEndPoint = WayPoint (Name=strRunWayEndPointName, LatitudeDegrees=self.runway.getLatitudeDegrees(), LongitudeDegrees=self.runway.getLongitudeDegrees(), AltitudeMeanSeaLevelMeters=self.airport.getFieldElevationAboveSeaLevelMeters()) ''' run-way true heading ''' runwayTrueHeadingDegrees = self.runway.getTrueHeadingDegrees() ''' call base class Graph to build Climb Ramp core of the route ''' index = 0 self.addVertex(runWayEndPoint) index += 1 ''' departure ground run => initial speed is null ''' trueAirSpeedMetersSecond = 0.1 ''' ground run leg distance ''' totalLegDistanceMeters = 0.0 self.aircraft.initStateVector( elapsedTimeSeconds, trueAirSpeedMetersSecond, self.airport.getFieldElevationAboveSeaLevelMeters()) ''' Usually, the lift-off speed is designated to be 1.2 * Vstall at a given weight, an aircraft will rotate and climb, stall or fly at an approach to landing at approx the same CAS. regardless of the elevation (height above sea level) , even though the true airspeed and ground-speed may differ significantly. These V speeds are normally published as IAS rather than CAS so they can be read directly from the airspeed indicator. ''' VStallSpeedCASKnots = self.aircraft.computeStallSpeedCasKnots() print ( self.className + ': V stall Calibrated AirSpeed= {0:.2f} knots'.format(VStallSpeedCASKnots) ) ''' loop until Stall CAS reached ''' endOfSimulation = False while ((endOfSimulation == False) and ( tas2cas(tas = trueAirSpeedMetersSecond , altitude = self.airport.getFieldElevationAboveSeaLevelMeters(), temp='std', speed_units = 'm/s', alt_units = 'm') * MeterPerSecond2Knots ) < (1.2 * VStallSpeedCASKnots)): ''' initial loop index ''' if index == 1: intermediateWayPoint = runWayEndPoint ''' fly => increase in true air speed ''' ''' during ground run => all the energy is used to increase the Kinetic energy => no potential energy increase ''' endOfSimulation, deltaDistanceMeters , altitudeMeters = self.aircraft.fly( elapsedTimeSeconds = elapsedTimeSeconds, deltaTimeSeconds = deltaTimeSeconds, distanceStillToFlyMeters = distanceStillToFlyMeters, currentPosition = intermediateWayPoint, distanceToLastFixMeters = distanceToLastFixMeters) trueAirSpeedMetersSecond = self.aircraft.getCurrentTrueAirSpeedMetersSecond() assert (((self.airport.getFieldElevationAboveSeaLevelMeters() - 10.0) <= altitudeMeters) and ( altitudeMeters <= (self.airport.getFieldElevationAboveSeaLevelMeters() + 10.0))) #print self.className + ': delta distance= ' + str(deltaDistanceMeters) + ' meters' # name of the next point totalLegDistanceMeters += deltaDistanceMeters distanceStillToFlyMeters -= deltaDistanceMeters distanceToLastFixMeters -= deltaDistanceMeters Name = '' if index == 1: Name = 'ground-run-pt-{0}-{1:.2f}-meters'.format(index-1, totalLegDistanceMeters) #bearingDegrees = math.fmod ( runwayTrueHeadingDegrees + 180.0 , 360.0 ) bearingDegrees = runwayTrueHeadingDegrees newIntermediateWayPoint = intermediateWayPoint.getWayPointAtDistanceBearing(Name = Name, DistanceMeters = deltaDistanceMeters, BearingDegrees = bearingDegrees) ''' during the ground run - altitude = airport field elevation ''' newIntermediateWayPoint.setAltitudeMeanSeaLevelMeters(self.airport.getFieldElevationAboveSeaLevelMeters()) ''' update route way-point ''' elapsedTimeSeconds += deltaTimeSeconds newIntermediateWayPoint.setElapsedTimeSeconds(elapsedTimeSeconds) self.elapsedTimeSeconds = elapsedTimeSeconds ''' insert in the route ''' self.addVertex(newIntermediateWayPoint) ''' copy the intermediate way-point ''' intermediateWayPoint = newIntermediateWayPoint ''' increment the index ''' index += 1 ''' rename last point as take-off ''' intermediateWayPoint.setName(Name = 'Take-Off-{0:.2f}-meters'.format(totalLegDistanceMeters)) def getElapsedTimeSeconds(self): return self.elapsedTimeSeconds
<filename>vectorai/utils.py """Miscellaneous functions for the client. """ import numpy as np import pandas as pd import itertools from functools import wraps import inspect import types import random from typing import List, Any, Dict, Union import warnings class UtilsMixin: """Various utilties """ def generate_vector(self, vector_length: int): """ Generate a random vector based on length Args: vector_length: Length of a vector. Example: >>> from vectorai.client import ViClient >>> vi_client = ViClient(username, api_key, vectorai_url) >>> vi_client.generate_vector(vector_length=20) """ return np.random.rand(vector_length).tolist() @staticmethod def results_to_df(data): """ Used for converting json responses of search results, etc Args: data: data returned from search, retrieve_documents, etc """ if "results" in data: return pd.DataFrame(data["results"]) elif "documents" in data: return pd.DataFrame(data["documents"]) elif type(data) == dict: return pd.DataFrame([data]) elif type(data) == list: return pd.DataFrame(data) else: print("missing field") def results_pretty(self, results: Union[Dict, List], field: str): """ Make results pretty in a Pandas Dataframe. Args: results: JSON of results field: Field to prettify results. Example: >>> result = vi_client.search(collection_name, text_encoder.encode('No more tissue paper'), field='OriginalTweet_vector_', page_size=5) >>> vi_client.results_pretty(result, 'OriginalTweet') """ pd.set_option('display.max_colwidth', None) if 'results' in results.keys(): results = results['results'] return pd.DataFrame({self.get_field(field, x) for x in results}, columns=[field]) @staticmethod def flatten_list(list_of_lists: List[List]): """ Flatten a nested list Args: list_of_lists: A list of lists to flatten to make into 1 list. Example: >>> from vectorai.client import ViClient >>> ViClient.flatten_list([[0, 1], [2, 3]]) """ return list(itertools.chain.from_iterable(list_of_lists)) @staticmethod def rename(object: Any, value: str): """ Rename an object. Args: object: Any Python object Example: >>> from vectorai.client import ViClient >>> def try_it: pass >>> ViClient.rename(try_it, 'new_name') """ object.__name__ = value @staticmethod def get_name(object): """ Get the name attribute of an object. Args: object: Any Python object Returns: object name: Name of object Example: >>> get_name(try_it) """ return object.__name__ @staticmethod def is_in_ipython(): """ Determines if current code is executed within an ipython session. """ is_in_ipython = False # Check if the runtime is within an interactive environment, i.e., ipython. try: from IPython import get_ipython # pylint: disable=import-error if get_ipython(): is_in_ipython = True except ImportError: pass # If dependencies are not available, then not interactive for sure. return is_in_ipython def is_in_notebook(self) -> bool: """ Determines if current code is executed from an ipython notebook. """ is_in_notebook = False if self.is_in_ipython(): # The import and usage must be valid under the execution path. from IPython import get_ipython if 'IPKernelApp' in get_ipython().config: is_in_notebook = True return is_in_notebook def progress_bar(self, documents: List, total: int=None): """ Returns a progress bar. depending on if notebook is available or not. Args: documents: A list of documents (Python dictionaries) Total: Length of documents/size of update Returns: Tqdm Progress Bar Example: for i in UtilsMixin.progress_bar(documents): ... """ if total is None: total = len(documents) if self.is_in_notebook(): from tqdm.notebook import tqdm return tqdm(documents, total=total) else: try: from tqdm import tqdm return tqdm(documents, total=total) except: return documents @classmethod def create_sample_document(self): """ Create sample document """ rand_index = np.random.randint(0, 30) return { 'color': [random.choice(['red', 'blue', 'orange', 'green'])], 'number': [random.choice(list(range(10)))], 'color_vector_': np.random.rand(1, 30).tolist()[0], 'size': { 'feet': list(range(1, 31))[rand_index], 'cm': (np.array(range(30)) * 30.48).tolist()[rand_index] } } @classmethod def create_sample_documents(self, num_of_documents: int): """ Create sample documents. Args: num_of_documents: Create sample documents. """ all_documents = [] for i in range(num_of_documents): document = self.create_sample_document() document['_id'] = str(i) all_documents.append(document) return all_documents def show_df(self, df: pd.DataFrame, image_fields: List[str]=[], audio_fields: List[str]=[], image_width: int=60): """ Shows a dataframe with the images and audio included inside the dataframe. Args: df: Pandas DataFrame image_fields: List of fields with the images audio_fields: List of fields for the audio nrows: Number of rows to preview image_width: The width of the images """ def path_to_image_html(path): return '<img src="'+ path + f'" width="{image_width}" >' def show_audio(x): return f"<audio controls><source src='{x}' type='audio/{self.get_audio_format(x)}'></audio>" formatters = {image:path_to_image_html for image in image_fields} formatters.update({audio: show_audio for audio in audio_fields}) try: from IPython.core.display import HTML return HTML(df.to_html(escape=False ,formatters=formatters)) except ImportError: return df def get_audio_format(self, string): if '.wav' in string: return 'wav' if '.mp3' in string: return 'mpeg' if '.ogg' in string: return 'ogg' warnings.warn("Unable to detect audio format. Must be either wav/mpeg/ogg.") return '' def show_json(self, json: dict, selected_fields: List[str]=[], image_fields: List[str]=[], audio_fields: List[str]=[], nrows: int=5, image_width: int=60): """ Shows the JSON with the audio and images inside a dataframe for quicker analysis. Args: json: Dictionary selected_fields: List of fields to see in the dictionary image_fields: List of fields with the images audio_fields: List of fields for the audio nrows: Number of rows to preview image_width: The width of the images """ if selected_fields == []: return self.show_df(self.results_to_df(json).head(nrows), image_fields=image_fields, audio_fields=audio_fields, image_width=image_width) return self.show_df(self.results_to_df(json).head(nrows)[image_fields + audio_fields + selected_fields], image_fields=image_fields, audio_fields=audio_fields, image_width=image_width) def decorate_functions_by_argument(function_decorator, argument): """ Decorate a function with an argument Args: function_decorator: A decorator function in Python argument: The Python argument that has to be altered Returns: A decorator """ def decorator(cls): for name, obj in inspect.getmembers(cls): if isinstance(obj, (types.MethodType, types.FunctionType)): if obj.__name__ == "__init__": continue if argument in inspect.signature(obj).parameters.keys(): try: obj = obj.__func__ # unwrap Python 2 unbound method except AttributeError: pass # not needed in Python 3 setattr(cls, name, function_decorator(obj, getattr(cls, argument))) return cls return decorator def get_stack_level() -> int: """ Return the stack level in Python. """ return len(inspect.stack(0)) def set_default_collection(func, collection_name): """ Set the default collection in functions Args: Func: The function which contains the collection_name argument Collection Name: The name of the collection Returns: Decorated function for default collection """ @wraps(func) def wrapper(*args, **kw): # Set a stack level so this only effects highest level functions # and not internal functions that use collection_name if 'collection_name' in inspect.getfullargspec(func).args \ and getattr(args[0], 'decorator_called') is False: setattr(args[0], 'decorator_called', True) setattr(args[0], 'decorator_call_stack_level', get_stack_level()) num_of_args = len(inspect.getfullargspec(func).args) collection_name = getattr(args[0], 'collection_name') if collection_name in kw.keys(): collection_name = kw.pop('collection_name') # from functools import partial # new_func = partial(func, collection_name=collection_name) # print(args) # res = new_func(*args, **kw) res = func(args[0], collection_name, *args[1:], **kw) # res = func(args[0], collection_name, *args[1:], **kw) # res = func(args[0], kw['collection_name'], *args[1:]) # except: # if len(args) == len(getfullargspec(func).args): # res = func(*args) # else: # try: # res = func(*args, **kw) # except TypeError: # kw.pop('collection_name') # res = func(*args, **kw) else: try: res = func(*args, **kw) except TypeError: collection_name = getattr(args[0], 'collection_name') res = func(args[0], collection_name, *args[1:], **kw) if get_stack_level() == getattr(args[0], 'decorator_call_stack_level') and \ getattr(args[0], 'decorator_called'): setattr(args[0], 'decorator_called', False) return res return wrapper
"""Cache lines z Python source files. This jest intended to read lines z modules imported -- hence jeżeli a filename is nie found, it will look down the module search path dla a file by that name. """ zaimportuj functools zaimportuj sys zaimportuj os zaimportuj tokenize __all__ = ["getline", "clearcache", "checkcache"] def getline(filename, lineno, module_globals=Nic): lines = getlines(filename, module_globals) jeżeli 1 <= lineno <= len(lines): zwróć lines[lineno-1] inaczej: zwróć '' # The cache # The cache. Maps filenames to either a thunk which will provide source code, # albo a tuple (size, mtime, lines, fullname) once loaded. cache = {} def clearcache(): """Clear the cache entirely.""" global cache cache = {} def getlines(filename, module_globals=Nic): """Get the lines dla a Python source file z the cache. Update the cache jeżeli it doesn't contain an entry dla this file already.""" jeżeli filename w cache: entry = cache[filename] jeżeli len(entry) != 1: zwróć cache[filename][2] spróbuj: zwróć updatecache(filename, module_globals) wyjąwszy MemoryError: clearcache() zwróć [] def checkcache(filename=Nic): """Discard cache entries that are out of date. (This jest nie checked upon each call!)""" jeżeli filename jest Nic: filenames = list(cache.keys()) inaczej: jeżeli filename w cache: filenames = [filename] inaczej: zwróć dla filename w filenames: entry = cache[filename] jeżeli len(entry) == 1: # lazy cache entry, leave it lazy. kontynuuj size, mtime, lines, fullname = entry jeżeli mtime jest Nic: continue # no-op dla files loaded via a __loader__ spróbuj: stat = os.stat(fullname) wyjąwszy OSError: usuń cache[filename] kontynuuj jeżeli size != stat.st_size albo mtime != stat.st_mtime: usuń cache[filename] def updatecache(filename, module_globals=Nic): """Update a cache entry oraz zwróć its list of lines. If something's wrong, print a message, discard the cache entry, oraz zwróć an empty list.""" jeżeli filename w cache: jeżeli len(cache[filename]) != 1: usuń cache[filename] jeżeli nie filename albo (filename.startswith('<') oraz filename.endswith('>')): zwróć [] fullname = filename spróbuj: stat = os.stat(fullname) wyjąwszy OSError: basename = filename # Realise a lazy loader based lookup jeżeli there jest one # otherwise try to lookup right now. jeżeli lazycache(filename, module_globals): spróbuj: data = cache[filename][0]() wyjąwszy (ImportError, OSError): dalej inaczej: jeżeli data jest Nic: # No luck, the PEP302 loader cannot find the source # dla this module. zwróć [] cache[filename] = ( len(data), Nic, [line+'\n' dla line w data.splitlines()], fullname ) zwróć cache[filename][2] # Try looking through the module search path, which jest only useful # when handling a relative filename. jeżeli os.path.isabs(filename): zwróć [] dla dirname w sys.path: spróbuj: fullname = os.path.join(dirname, basename) wyjąwszy (TypeError, AttributeError): # Not sufficiently string-like to do anything useful with. kontynuuj spróbuj: stat = os.stat(fullname) przerwij wyjąwszy OSError: dalej inaczej: zwróć [] spróbuj: przy tokenize.open(fullname) jako fp: lines = fp.readlines() wyjąwszy OSError: zwróć [] jeżeli lines oraz nie lines[-1].endswith('\n'): lines[-1] += '\n' size, mtime = stat.st_size, stat.st_mtime cache[filename] = size, mtime, lines, fullname zwróć lines def lazycache(filename, module_globals): """Seed the cache dla filename przy module_globals. The module loader will be asked dla the source only when getlines jest called, nie immediately. If there jest an entry w the cache already, it jest nie altered. :return: Prawda jeżeli a lazy load jest registered w the cache, otherwise Nieprawda. To register such a load a module loader przy a get_source method must be found, the filename must be a cachable filename, oraz the filename must nie be already cached. """ jeżeli filename w cache: jeżeli len(cache[filename]) == 1: zwróć Prawda inaczej: zwróć Nieprawda jeżeli nie filename albo (filename.startswith('<') oraz filename.endswith('>')): zwróć Nieprawda # Try dla a __loader__, jeżeli available jeżeli module_globals oraz '__loader__' w module_globals: name = module_globals.get('__name__') loader = module_globals['__loader__'] get_source = getattr(loader, 'get_source', Nic) jeżeli name oraz get_source: get_lines = functools.partial(get_source, name) cache[filename] = (get_lines,) zwróć Prawda zwróć Nieprawda
import pygame; WHITE = (255, 255, 255) BLACK = (0, 0, 0) GRAY = (30, 30, 30) pygame.mixer.pre_init(44100, 16, 2, 4096); pygame.init(); SONG_END = pygame.USEREVENT + 1 DefaultFont = pygame.font.Font('freesansbold.ttf', 50); ### INPUT DEVICES ### class PyMouse: def __init__( self ): self.pos = (0, 0); self.x = self.pos[0]; self.y = self.pos[1]; self.isDown = False; class PyKeyboard: def __init__( self ): self.keys = []; self.clipboard = ""; self.types = ""; def keyDown(self, key): if key in self.keys: return True; else: return False; def clipboardDecode(self): types = pygame.scrap.get_types(); if(types == []): return "Empty"; Type = types[0]; string = pygame.scrap.get(Type); if(Type == 'HTML Format'): return "Image"; else: decoded = string.decode("utf-16").rstrip('\x00') return decoded; def updateInput(Mouse, Keyboard, Sound): Mouse.pos = pygame.mouse.get_pos(); Mouse.x = Mouse.pos[0]; Mouse.y = Mouse.pos[1]; Keyboard.clipboard = Keyboard.clipboardDecode(); for event in pygame.event.get(): ### MOUSE UPDATES ### if event.type == pygame.MOUSEBUTTONDOWN: if event.button == 1: Mouse.isDown = True; if event.type == pygame.MOUSEBUTTONUP: Mouse.isDown = False; ### KEYBOARD UPDATES ### if event.type == pygame.KEYDOWN: Keyboard.keys.append(pygame.key.name(event.key)); if event.type == pygame.KEYUP: Keyboard.keys.remove(pygame.key.name(event.key)); ### SOUND EVENTS ### if event.type == SONG_END: Sound.Stop(); ### MISC ### class PySound: def __init__(self): self.Channel = pygame.mixer.Channel(2); self.Channel.set_endevent(SONG_END); self.volume = 1; self.isPlaying = False; self.currentSound = None; self.loadedSounds = { }; def Load(self, path): return pygame.mixer.Sound(path); def Play(self, path, times = 0): self.Channel.set_volume(self.volume); if path not in self.loadedSounds: self.loadedSounds[path] = self.Load(path); if(self.isPlaying): return; self.Channel.play(self.loadedSounds[path], times); self.isPlaying = True self.currentSound = path; def Stop(self): self.isPlaying = False; self.Channel.stop(); self.currentSound = None; ### OBJECT DRAWING ### class PyImage: def __init__( self, screen, path, x, y, width, height): self.isClicked = False; self.screen = screen.screen; self.width, self.height = width, height; self.pos = (x, y); self.imagePath = path; self.Draw(); screen.objects.append(self); def Draw(self): self.x, self.y = self.pos[0], self.pos[1]; self.image = pygame.image.load(self.imagePath); self.image = pygame.transform.scale(self.image, (self.width, self.height)) self.rect = self.image.get_rect(); self.rect = self.rect.move(self.pos) self.screen.blit(self.image, self.rect); class PyRect: def __init__( self, screen, color, x, y, width, height): self.isClicked = False; self.mouseState = None; self.screen = screen.screen; self.width, self.height, self.color = width, height, color self.pos = (x, y); self.Draw(); screen.objects.append(self); def Draw(self): self.x, self.y = self.pos[0], self.pos[1]; self.rect = pygame.Rect(self.x, self.y, self.width, self.height); pygame.draw.rect(self.screen, self.color, self.rect); class PyText: def __init__(self, screen, color, text, x, y, fontSize = 24): self.font = pygame.font.Font('freesansbold.ttf', fontSize); self.text = text; self.color = color; self.pos = (x, y); self.screen = screen.screen; self.Draw(); screen.objects.append(self); def Draw(self): self.x, self.y = self.pos[0], self.pos[1]; self.textSurface = self.font.render(self.text, True, self.color); self.rect = self.textSurface.get_rect(); self.rect.center = self.pos; self.screen.blit(self.textSurface, self.rect); ### SCREEN CLASS ### class PyScreen: def __init__(self, caption, color, width, height): self.width = width; self.height = height; self.color = color; self.mouse = PyMouse(); self.keyboard = PyKeyboard(); self.sound = PySound(); self.caption = caption; self.objects = []; self.startScreen(); def startScreen(self): pygame.display.set_caption(self.caption); self.screen = pygame.display.set_mode((self.width, self.height)) pygame.scrap.init(); def Exit(self): pygame.quit(); def Wait(self, seconds): pygame.time.wait(seconds*1000); def updateScreen(self): pygame.display.flip() pygame.display.set_caption(self.caption) self.screen.fill(self.color); updateInput(self.mouse, self.keyboard, self.sound); for x in self.objects: if(x.rect.collidepoint(self.mouse.pos) and self.mouse.isDown): x.isClicked = True; else: x.isClicked = False; x.Draw();
<reponame>meyer-lab/bi-cytok<filename>ckine/figures/figure3.py """ This creates Figure 1, response of bispecific IL-2 cytokines at varing valencies and abundances using binding model. """ from .figureCommon import getSetup from ..imports import importCITE from sklearn.decomposition import PCA from copy import copy import pandas as pd import seaborn as sns import numpy as np from sklearn.preprocessing import StandardScaler from sklearn.linear_model import RidgeClassifierCV from sklearn.preprocessing import LabelEncoder from sklearn.manifold import TSNE def makeFigure(): """Get a list of the axis objects and create a figure""" ax, f = getSetup((9, 12), (5, 2)) cellTarget = "Treg" ax[1].axis("off") CITE_TSNE(ax[0], sampleFrac=0.4) legend = ax[0].get_legend() labels = (x.get_text() for x in legend.get_texts()) ax[1].legend(legend.legendHandles, labels, loc="upper left", prop={"size": 10}, ncol=3) ax[0].get_legend().remove() posCorrs, negCorrs = CITE_RIDGE(ax[4], cellTarget) CITE_PCA(ax[2:4], posCorrs, negCorrs) RIDGE_Scatter(ax[5], posCorrs, negCorrs) distMetricScatt(ax[6:8], cellTarget, 10, weight=False) distMetricScatt(ax[8:10], cellTarget, 10, weight=True) return f def CITE_TSNE(ax, sampleFrac): """ Plots all surface markers in PCA format""" TSNE_DF = importCITE() cellToI = TSNE_DF.CellType2.unique() TSNE_DF = TSNE_DF.loc[(TSNE_DF["CellType2"].isin(cellToI)), :] TSNE_DF = TSNE_DF.sample(frac=sampleFrac, random_state=1) cellType = TSNE_DF.CellType2.values TSNE_DF = TSNE_DF.loc[:, ((TSNE_DF.columns != 'CellType1') & (TSNE_DF.columns != 'CellType2') & (TSNE_DF.columns != 'CellType3') & (TSNE_DF.columns != 'Cell'))] factors = TSNE_DF.columns scaler = StandardScaler() TSNE_Arr = scaler.fit_transform(X=TSNE_DF.values) pca = PCA(n_components=20) TSNE_PCA_Arr = pca.fit_transform(TSNE_Arr) X_embedded = TSNE(n_components=2, verbose=1, perplexity=50, n_iter=1000, learning_rate=200).fit_transform(TSNE_PCA_Arr) df_tsne = pd.DataFrame(X_embedded, columns=['comp1', 'comp2']) df_tsne['label'] = cellType sns.scatterplot(x='comp1', y='comp2', data=df_tsne, hue='label', alpha=0.3, ax=ax, s=2) def CITE_PCA(ax, posCorrs, negCorrs): """ Plots all surface markers in PCA format""" PCA_DF = importCITE() cellToI = PCA_DF.CellType2.unique() PCA_DF = PCA_DF.loc[(PCA_DF["CellType2"].isin(cellToI)), :] cellType = PCA_DF.CellType2.values PCA_DF = PCA_DF.loc[:, ((PCA_DF.columns != 'CellType1') & (PCA_DF.columns != 'CellType2') & (PCA_DF.columns != 'CellType3') & (PCA_DF.columns != 'Cell'))] factors = PCA_DF.columns scaler = StandardScaler() pca = PCA(n_components=10) PCA_Arr = scaler.fit_transform(X=PCA_DF.values) pca.fit(PCA_Arr) comps = pca.components_ loadingsDF = pd.DataFrame({"PC 1": comps[0], "PC 2": comps[1], "Factors": factors}) loadP = sns.scatterplot(data=loadingsDF, x="PC 1", y="PC 2", ax=ax[1]) for line in range(0, loadingsDF.shape[0]): if factors[line] in posCorrs: loadP.text(loadingsDF["PC 1"][line] + 0.002, loadingsDF["PC 2"][line], factors[line], horizontalalignment='left', size='medium', color='green', weight='semibold') if factors[line] in negCorrs: loadP.text(loadingsDF["PC 1"][line] + 0.002, loadingsDF["PC 2"][line], factors[line], horizontalalignment='left', size='medium', color='red', weight='semibold') scores = pca.transform(PCA_Arr) scoresDF = pd.DataFrame({"PC 1": scores[:, 0], "PC 2": scores[:, 1], "Cell Type": cellType}) centerDF = pd.DataFrame(columns=["PC 1", "PC 2", "Cell Type"]) for cell in cellToI: cellTDF = scoresDF.loc[scoresDF["Cell Type"] == cell] centerDF = centerDF.append(pd.DataFrame({"PC 1": [cellTDF["PC 1"].mean()], "PC 2": [cellTDF["PC 2"].mean()], "Cell Type": [cell]})) sns.scatterplot(data=centerDF, x="PC 1", y="PC 2", ax=ax[0], hue="Cell Type", legend=False) ax[0].set(xlim=(-10, 30), ylim=(-5, 5)) def CITE_RIDGE(ax, targCell, numFactors=10): """Fits a ridge classifier to the CITE data and plots those most highly correlated with T reg""" ridgeMod = RidgeClassifierCV() RIDGE_DF = importCITE() cellToI = RIDGE_DF.CellType2.unique() RIDGE_DF = RIDGE_DF.loc[(RIDGE_DF["CellType2"].isin(cellToI)), :] cellTypeCol = RIDGE_DF.CellType2.values RIDGE_DF = RIDGE_DF.loc[:, ((RIDGE_DF.columns != 'CellType1') & (RIDGE_DF.columns != 'CellType2') & (RIDGE_DF.columns != 'CellType3') & (RIDGE_DF.columns != 'Cell'))] factors = RIDGE_DF.columns X = RIDGE_DF.values X = StandardScaler().fit_transform(X) le = LabelEncoder() le.fit(cellTypeCol) y = le.transform(cellTypeCol) ridgeMod = RidgeClassifierCV(cv=5) ridgeMod.fit(X, y) TargCoefs = ridgeMod.coef_[np.where(le.classes_ == targCell), :].ravel() TargCoefsDF = pd.DataFrame({"Marker": factors, "Coefficient": TargCoefs}).sort_values(by="Coefficient") TargCoefsDF = pd.concat([TargCoefsDF.head(numFactors), TargCoefsDF.tail(numFactors)]) sns.barplot(data=TargCoefsDF, x="Marker", y="Coefficient", ax=ax) ax.set_xticklabels(ax.get_xticklabels(), rotation=45) posCorrs = TargCoefsDF.tail(numFactors).Marker.values negCorrs = TargCoefsDF.head(numFactors).Marker.values print(posCorrs) return posCorrs, negCorrs def RIDGE_Scatter(ax, posCorrs, negCorrs): """Fits a ridge classifier to the CITE data and plots those most highly correlated with T reg""" CITE_DF = importCITE() cellToI = CITE_DF.CellType2.unique() CITE_DF = CITE_DF.loc[(CITE_DF["CellType2"].isin(cellToI)), :] cellTypeCol = CITE_DF.CellType2.values CITE_DF = CITE_DF[np.append(negCorrs, posCorrs)] CITE_DF["Cell Type"] = cellTypeCol CITE_DF = pd.melt(CITE_DF, id_vars="Cell Type", var_name="Marker", value_name='Amount') sns.pointplot(data=CITE_DF, x="Marker", y="Amount", hue="Cell Type", ax=ax, join=False, dodge=True, legend=False) ax.set(yscale="log") ax.set_xticklabels(ax.get_xticklabels(), rotation=45) ax.get_legend().remove() def distMetricScatt(ax, targCell, numFactors, weight=False): """Finds markers which have average greatest difference from other cells""" CITE_DF = importCITE() cellToI = CITE_DF.CellType2.unique() offTargs = copy(cellToI) offTargs = np.delete(offTargs, np.where(offTargs == targCell)) CITE_DF = CITE_DF.loc[(CITE_DF["CellType2"].isin(cellToI)), :] cellTypeCol = CITE_DF.CellType2.values markerDF = pd.DataFrame(columns=["Marker", "Cell Type", "Amount"]) for marker in CITE_DF.loc[:, ((CITE_DF.columns != 'CellType1') & (CITE_DF.columns != 'CellType2') & (CITE_DF.columns != 'CellType3') & (CITE_DF.columns != 'Cell'))].columns: for cell in cellToI: cellTDF = CITE_DF.loc[CITE_DF["CellType2"] == cell][marker] markerDF = markerDF.append(pd.DataFrame({"Marker": [marker], "Cell Type": cell, "Amount": cellTDF.mean(), "Number": cellTDF.size})) ratioDF = pd.DataFrame(columns=["Marker", "Ratio"]) for marker in CITE_DF.loc[:, ((CITE_DF.columns != 'CellType1') & (CITE_DF.columns != 'CellType2') & (CITE_DF.columns != 'CellType3') & (CITE_DF.columns != 'Cell'))].columns: if weight: offT = 0 targ = markerDF.loc[(markerDF["Cell Type"] == targCell) & (markerDF["Marker"] == marker)].Amount.mean() for cell in offTargs: offT += markerDF.loc[(markerDF["Cell Type"] == cell) & (markerDF["Marker"] == marker)].Amount.mean() ratioDF = ratioDF.append(pd.DataFrame({"Marker": [marker], "Ratio": (targ * len(offTargs)) / offT})) else: offT = 0 targ = markerDF.loc[(markerDF["Cell Type"] == targCell) & (markerDF["Marker"] == marker)].Amount.values * \ markerDF.loc[(markerDF["Cell Type"] == targCell) & (markerDF["Marker"] == marker)].Number.values for cell in offTargs: offT += markerDF.loc[(markerDF["Cell Type"] == cell) & (markerDF["Marker"] == marker)].Amount.values * \ markerDF.loc[(markerDF["Cell Type"] == cell) & (markerDF["Marker"] == marker)].Number.values ratioDF = ratioDF.append(pd.DataFrame({"Marker": [marker], "Ratio": (targ * len(offTargs)) / offT})) ratioDF = ratioDF.sort_values(by="Ratio") posCorrs = ratioDF.tail(numFactors).Marker.values markerDF = markerDF.loc[markerDF["Marker"].isin(posCorrs)] sns.barplot(data=ratioDF.tail(numFactors), x="Marker", y="Ratio", ax=ax[0]) ax[0].set(yscale="log") ax[0].set_xticklabels(ax[0].get_xticklabels(), rotation=45) if weight: ax[0].set(title="Ratios Weighted by Cell Type") else: ax[0].set(title="Ratios Weighted by Number of Cells") sns.pointplot(data=markerDF, x="Marker", y="Amount", hue="Cell Type", ax=ax[1], join=False, dodge=True, order=posCorrs, legend=False) ax[1].set(yscale="log") ax[1].set_xticklabels(ax[1].get_xticklabels(), rotation=45) if weight: ax[1].set(title="Markers Weighted by Cell Type") else: ax[1].set(title="Markers Weighted by Number of Cells") ax[1].get_legend().remove()
import os import ssl import unittest from unittest.mock import patch import asyncio from .client import * from .exceptions import InvalidHandshake from .http import read_response, USER_AGENT from .server import * testcert = os.path.join(os.path.dirname(__file__), 'testcert.pem') @asyncio.coroutine def handler(ws, path): if path == '/attributes': yield from ws.send(repr((ws.host, ws.port, ws.secure))) elif path == '/raw_headers': yield from ws.send(repr(ws.raw_request_headers)) yield from ws.send(repr(ws.raw_response_headers)) else: yield from ws.send((yield from ws.recv())) class ClientServerTests(unittest.TestCase): secure = False def setUp(self): self.loop = asyncio.new_event_loop() asyncio.set_event_loop(self.loop) self.start_server() def tearDown(self): self.stop_server() self.loop.close() def start_server(self): server = serve(handler, 'localhost', 8642) self.server = self.loop.run_until_complete(server) def start_client(self, path=''): client = connect('ws://localhost:8642/' + path) self.client = self.loop.run_until_complete(client) def stop_client(self): self.loop.run_until_complete(self.client.worker) def stop_server(self): self.server.close() self.loop.run_until_complete(self.server.wait_closed()) def test_basic(self): self.start_client() self.loop.run_until_complete(self.client.send("Hello!")) reply = self.loop.run_until_complete(self.client.recv()) self.assertEqual(reply, "Hello!") self.stop_client() def test_protocol_attributes(self): self.start_client('attributes') expected_attrs = ('localhost', 8642, self.secure) client_attrs = (self.client.host, self.client.port, self.client.secure) self.assertEqual(client_attrs, expected_attrs) server_attrs = self.loop.run_until_complete(self.client.recv()) self.assertEqual(server_attrs, repr(expected_attrs)) self.stop_client() def test_protocol_raw_headers(self): self.start_client('raw_headers') client_req = self.client.raw_request_headers client_resp = self.client.raw_response_headers self.assertEqual(dict(client_req)['User-Agent'], USER_AGENT) self.assertEqual(dict(client_resp)['Server'], USER_AGENT) server_req = self.loop.run_until_complete(self.client.recv()) server_resp = self.loop.run_until_complete(self.client.recv()) self.assertEqual(server_req, repr(client_req)) self.assertEqual(server_resp, repr(client_resp)) self.stop_client() @patch('websockets.server.read_request') def test_server_receives_malformed_request(self, _read_request): _read_request.side_effect = ValueError("read_request failed") with self.assertRaises(InvalidHandshake): self.start_client() @patch('websockets.client.read_response') def test_client_receives_malformed_response(self, _read_response): _read_response.side_effect = ValueError("read_response failed") with self.assertRaises(InvalidHandshake): self.start_client() # Now the server believes the connection is open. Run the event loop # once to make it notice the connection was closed. Interesting hack. self.loop.run_until_complete(asyncio.sleep(0)) @patch('websockets.client.build_request') def test_client_sends_invalid_handshake_request(self, _build_request): def wrong_build_request(set_header): return '42' _build_request.side_effect = wrong_build_request with self.assertRaises(InvalidHandshake): self.start_client() @patch('websockets.server.build_response') def test_server_sends_invalid_handshake_response(self, _build_response): def wrong_build_response(set_header, key): return build_response(set_header, '42') _build_response.side_effect = wrong_build_response with self.assertRaises(InvalidHandshake): self.start_client() @patch('websockets.client.read_response') def test_server_does_not_switch_protocols(self, _read_response): @asyncio.coroutine def wrong_read_response(stream): code, headers = yield from read_response(stream) return 400, headers _read_response.side_effect = wrong_read_response with self.assertRaises(InvalidHandshake): self.start_client() # Now the server believes the connection is open. Run the event loop # once to make it notice the connection was closed. Interesting hack. self.loop.run_until_complete(asyncio.sleep(0)) @patch('websockets.server.WebSocketServerProtocol.send') def test_server_handler_crashes(self, send): send.side_effect = ValueError("send failed") self.start_client() self.loop.run_until_complete(self.client.send("Hello!")) reply = self.loop.run_until_complete(self.client.recv()) self.assertEqual(reply, None) self.stop_client() # Connection ends with an unexpected error. self.assertEqual(self.client.close_code, 1011) @patch('websockets.server.WebSocketServerProtocol.close') def test_server_close_crashes(self, close): close.side_effect = ValueError("close failed") self.start_client() self.loop.run_until_complete(self.client.send("Hello!")) reply = self.loop.run_until_complete(self.client.recv()) self.assertEqual(reply, "Hello!") self.stop_client() # Connection ends with a protocol error. self.assertEqual(self.client.close_code, 1002) @unittest.skipUnless(os.path.exists(testcert), "test certificate is missing") class SSLClientServerTests(ClientServerTests): secure = True @property def server_context(self): ssl_context = ssl.SSLContext(ssl.PROTOCOL_TLSv1) ssl_context.load_cert_chain(testcert) return ssl_context @property def client_context(self): ssl_context = ssl.SSLContext(ssl.PROTOCOL_TLSv1) ssl_context.load_verify_locations(testcert) ssl_context.verify_mode = ssl.CERT_REQUIRED return ssl_context def start_server(self): server = serve(handler, 'localhost', 8642, ssl=self.server_context) self.server = self.loop.run_until_complete(server) def start_client(self, path=''): client = connect('wss://localhost:8642/' + path, ssl=self.client_context) self.client = self.loop.run_until_complete(client) def test_ws_uri_is_rejected(self): client = connect('ws://localhost:8642/', ssl=self.client_context) with self.assertRaises(ValueError): self.loop.run_until_complete(client) class ClientServerOriginTests(unittest.TestCase): def test_checking_origin_succeeds(self): loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) server = loop.run_until_complete( serve(handler, 'localhost', 8642, origins=['http://localhost'])) client = loop.run_until_complete( connect('ws://localhost:8642/', origin='http://localhost')) loop.run_until_complete(client.send("Hello!")) self.assertEqual(loop.run_until_complete(client.recv()), "Hello!") server.close() loop.run_until_complete(server.wait_closed()) loop.run_until_complete(client.worker) loop.close() def test_checking_origin_fails(self): loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) server = loop.run_until_complete( serve(handler, 'localhost', 8642, origins=['http://localhost'])) with self.assertRaises(InvalidHandshake): loop.run_until_complete( connect('ws://localhost:8642/', origin='http://otherhost')) server.close() loop.run_until_complete(server.wait_closed()) loop.close() def test_checking_lack_of_origin_succeeds(self): loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) server = loop.run_until_complete( serve(handler, 'localhost', 8642, origins=[''])) client = loop.run_until_complete(connect('ws://localhost:8642/')) loop.run_until_complete(client.send("Hello!")) self.assertEqual(loop.run_until_complete(client.recv()), "Hello!") server.close() loop.run_until_complete(server.wait_closed()) loop.run_until_complete(client.worker) loop.close()
from enum import IntFlag from .chinese.extractors import * from recognizers_text import * from .english.extractors import * from .english.parsers import * from .models import * from .parsers import * class SequenceOptions(IntFlag): NONE = 0 class SequenceRecognizer(Recognizer[SequenceOptions]): def __init__(self, target_culture: str = None, options: SequenceOptions = SequenceOptions.NONE, lazy_initialization: bool = True): if options < SequenceOptions.NONE or options > SequenceOptions.NONE: raise ValueError() super().__init__(target_culture, options, lazy_initialization) def initialize_configuration(self): self.register_model('PhoneNumberModel', Culture.English, lambda options: PhoneNumberModel(PhoneNumberParser(), BasePhoneNumberExtractor(EnglishPhoneNumberExtractorConfiguration()))) self.register_model('EmailModel', Culture.English, lambda options: EmailModel(EmailParser(), EnglishEmailExtractor())) self.register_model('PhoneNumberModel', Culture.Chinese, lambda options: PhoneNumberModel(PhoneNumberParser(), BasePhoneNumberExtractor(ChinesePhoneNumberExtractorConfiguration()))) self.register_model('IpAddressModel', Culture.English, lambda options: IpAddressModel(IpParser(), EnglishIpExtractor())) self.register_model('MentionModel', Culture.English, lambda options: MentionModel(MentionParser(), EnglishMentionExtractor())) self.register_model('HashtagModel', Culture.English, lambda options: HashtagModel(HashtagParser(), EnglishHashtagExtractor())) self.register_model('URLModel', Culture.English, lambda options: URLModel( URLParser(), BaseURLExtractor(EnglishURLExtractorConfiguration(options))) ) self.register_model('URLModel', Culture.Chinese, lambda options: URLModel( URLParser(), BaseURLExtractor(ChineseURLExtractorConfiguration(options))) ) self.register_model('GUIDModel', Culture.English, lambda options: GUIDModel(GUIDParser(), EnglishGUIDExtractor())) def get_phone_number_model(self, culture: str = None, fallback_to_default_culture: bool = True) -> Model: if culture and (culture.lower().startswith("zh-") or culture.lower().startswith("ja-")): return self.get_model('PhoneNumberModel', Culture.Chinese, fallback_to_default_culture) return self.get_model('PhoneNumberModel', culture, fallback_to_default_culture) def get_ip_address_model(self, culture: str = None, fallback_to_default_culture: bool = True) -> Model: return self.get_model('IpAddressModel', culture, fallback_to_default_culture) def get_mention_model(self, culture: str = None, fallback_to_default_culture: bool = True) -> Model: return self.get_model('MentionModel', culture, fallback_to_default_culture) def get_hashtag_model(self, culture: str = None, fallback_to_default_culture: bool = True) -> Model: return self.get_model('HashtagModel', culture, fallback_to_default_culture) def get_url_model(self, culture: str = None, fallback_to_default_culture: bool = True) -> Model: if culture and (culture.lower().startswith("zh-") or culture.lower().startswith("ja-")): return self.get_model('URLModel', Culture.Chinese, fallback_to_default_culture) return self.get_model('URLModel', culture, fallback_to_default_culture) def get_guid_model(self, culture: str = None, fallback_to_default_culture: bool = True) -> Model: return self.get_model('GUIDModel', culture, fallback_to_default_culture) def get_email_model(self, culture: str = None, fallback_to_default_culture: bool = True) -> Model: return self.get_model('EmailModel', culture, fallback_to_default_culture) def recognize_phone_number(query: str, culture: str, options: SequenceOptions = SequenceOptions.NONE, fallback_to_default_culture: bool = True) -> List[ModelResult]: recognizer = SequenceRecognizer(culture, options) model = recognizer.get_phone_number_model(culture, fallback_to_default_culture) return model.parse(query) def recognize_email(query: str, culture: str, options: SequenceOptions = SequenceOptions.NONE, fallback_to_default_culture: bool = True) -> List[ModelResult]: recognizer = SequenceRecognizer(culture, options) model = recognizer.get_email_model(culture, fallback_to_default_culture) return model.parse(query) def recognize_ip_address(query: str, culture: str, options: SequenceOptions = SequenceOptions.NONE, fallback_to_default_culture: bool = True) -> List[ModelResult]: recognizer = SequenceRecognizer(culture, options) model = recognizer.get_ip_address_model(culture, fallback_to_default_culture) return model.parse(query) def recognize_mention(query: str, culture: str, options: SequenceOptions = SequenceOptions.NONE, fallback_to_default_culture: bool = True) -> List[ModelResult]: recognizer = SequenceRecognizer(culture, options) model = recognizer.get_mention_model(culture, fallback_to_default_culture) return model.parse(query) def recognize_hashtag(query: str, culture: str, options: SequenceOptions = SequenceOptions.NONE, fallback_to_default_culture: bool = True) -> List[ModelResult]: recognizer = SequenceRecognizer(culture, options) model = recognizer.get_hashtag_model(culture, fallback_to_default_culture) return model.parse(query) def recognize_url(query: str, culture: str, options: SequenceOptions = SequenceOptions.NONE, fallback_to_default_culture: bool = True) -> List[ModelResult]: recognizer = SequenceRecognizer(culture, options) model = recognizer.get_url_model(culture, fallback_to_default_culture) return model.parse(query) def recognize_guid(query: str, culture: str, options: SequenceOptions = SequenceOptions.NONE, fallback_to_default_culture: bool = True) -> List[ModelResult]: recognizer = SequenceRecognizer(culture, options) model = recognizer.get_guid_model(culture, fallback_to_default_culture) return model.parse(query)
"""Contains dataset importers for NYU Depth Dataset V2 and SYNTHIA-SF""" from __future__ import absolute_import, division, print_function import os import numpy as np import pandas as pd import tables from skimage import img_as_float32 from skimage import img_as_float64 from skimage.io import imread from skimage.transform import resize from skimage import img_as_ubyte from skimage import img_as_uint import segmentation_dics IMAGE = 0 SEGMENTATION = 1 INSTANCE = 2 DEPTH = 3 TRAIN = 0 VALIDATION = 1 TEST = 2 class DatasetGenerator: """Abstract iterator for looping over elements of a dataset . Arguments: ratio: ratio of the train-set size to the validation-set size and test-set size The first number is for the train-set, the second is for validation-set and what is remained is for test-set.(the sum of two numbers should equal to one or less) batch_size: Integer batch size. repeater: If true, the dataset generator starts generating samples from the beginning when it reaches the end of the dataset. shuffle: Boolean. Whether to shuffle the order of the batches at the beginning of the training. output_shape: size of generated images and labels. data_type: data type of features. label_type: Types of labels to be returned. """ def __init__(self, usage='train', ratio=(1, 0), batch_size=1, repeater=False, shuffle=True, output_shape=None, data_type='float64', label_type=('segmentation', 'instance', 'depth'), **kwargs): self.ratio = kwargs[ 'ratio'] if 'ratio' in kwargs else ratio self.batch_size = kwargs[ 'batch_size'] if 'batch_size' in kwargs else batch_size self.repeater = kwargs['repeater'] if 'repeater' in kwargs else repeater self.shuffle = kwargs['shuffle'] if 'shuffle' in kwargs else shuffle self.output_shape = kwargs[ 'output_shape'] if 'output_shape' in kwargs else output_shape self.data_type = kwargs[ 'data_type'] if 'data_type' in kwargs else data_type self.label_type = kwargs[ 'label_type'] if 'label_type' in kwargs else label_type self.dataset = self.data_frame_creator() self.size = self.dataset.shape[0] - 1 self.start_index = 0 self.end_index = np.int32(np.floor(self.ratio[TRAIN] * self.size)) self.dataset_usage(usage) self.index = self.start_index def data_frame_creator(self): """Pandas dataFrame for addresses of images and corresponding labels""" return pd.DataFrame() def dataset_usage(self, usage): """ Determines the current usage of the dataset: - 'train' - 'validation' - 'test' """ if usage is 'train': self.start_index = 0 self.end_index = np.int32(np.floor(self.ratio[TRAIN] * self.size)) elif usage is 'validation': self.start_index = np.int32(np.floor(self.ratio[TRAIN] * self.size)) self.end_index = np.int32(np.floor((self.ratio[TRAIN] + self.ratio[VALIDATION])* self.size)) elif usage is 'test': self.start_index = np.int32(np.floor((self.ratio[TRAIN] + self.ratio[VALIDATION])* self.size)) self.end_index = self.size else: print('Invalid input for usage variable') raise NameError('InvalidInput') def __iter__(self): return self def __next__(self): return self.next() def next(self): """Retrieve the next pairs from the dataset""" if self.index - self.batch_size <= 0: if not self.repeater: raise StopIteration else: self.index = self.dataset.shape[0] - 1 self.index = self.index - self.batch_size # loading features(images) features = imread(self.dataset.loc[:, 'image'].iat[0])[:, :, :3] if self.output_shape is None: output_shape = features.shape[:2] else: output_shape = self.output_shape # 1) Resize image to match a certain size. # 2) Also the input image is converted (from 8-bit integer) # to 64-bit floating point(->preserve_range=False). # 3) [:, :, :3] -> to remove 4th channel in png features = np.array([ resize(image=imread(self.dataset.loc[:, 'image'].iat[i])[:, :, :3], output_shape=output_shape, mode='constant', preserve_range=False, anti_aliasing=True) for i in range(self.index, self.index + self.batch_size) ]) if self.data_type is 'float32': features = img_as_float32(features) # loading labels(segmentation) if 'segmentation' in self.label_type: # 1) Resize segmentation to match a certain size. # 2) [:, :, :3] -> to remove 4th channel in png segmentation = np.array([ imread(self.dataset.loc[:, 'segmentation'].iat[i])[:, :, :3] for i in range(self.index, self.index + self.batch_size) ]) # resize(image=, # output_shape=output_shape, # mode='constant', # preserve_range=True, # anti_aliasing=True) # new_segmentation = np.zeros( # shape=(self.batch_size, output_shape[0], output_shape[1], # len(self.seg_dic))) # for i in range(self.batch_size): # for j in range(output_shape[0]): # for k in range(output_shape[1]): # new_segmentation[i, j, k, # self.seg_dic[ # tuple(segmentation[i, j, k]) ][0]] = 1 # segmentation = new_segmentation if self.data_type is 'float32': segmentation = img_as_float32(segmentation) else: segmentation = img_as_float64(segmentation) # if self.label_type == 'depth': # labels = np.array( # np.array([ # resize( # image=imread(self.dataset.iloc[i, 1]), # output_shape=(480, 640)) # for i in range(self.index, self.index + self.batch_size) # ]), # dtype=np.int32) # labels = (labels[:, :, :, 0] + labels[:, :, :, 1] * 256 + # labels[:, :, :, 2] * 256 * 256) / ((256 * 256 * 256) - 1) # elif self.label_type == 'segmentation': # labels = np.array( # np.array([ # resize( # image=imread(self.dataset.iloc[i, 2])[:, :, 0], # output_shape=(480, 640)) # for i in range(self.index, self.index + self.batch_size) # ])) # new_segmentation = np.ndarray(shape=(self.batch_size, 480, 640, 22)) # for i in range(self.batch_size): # for j in range(480): # for k in range(640): # if labels[i, j, k] < 22: # new_segmentation[i, j, k, int(labels[i, j, k])] = 1 # labels = new_segmentation # labels = np.array(labels, dtype=np.float32) # else: # raise ValueError('invalid label type') # return features, labels return features, segmentation class NewSynthiaSf(DatasetGenerator): """Iterator for looping over elements of SYNTHIA-SF backwards.""" def __init__(self, synthia_sf_dir, **kwargs): self.dataset_dir = synthia_sf_dir self.max_distance = 1000 super().__init__(**kwargs) def data_frame_creator(self): """ pandas dataFrame for addresses of rgb, depth and segmentation""" sequence_folder = [ '/SEQ1', '/SEQ2', '/SEQ3', '/SEQ4', '/SEQ5', '/SEQ6' ] rgb_folder = ['/RGBLeft/', '/RGBRight/'] depth_folder = ['/DepthLeft/', '/DepthRight/'] segmentation_folder = ['/GTLeft/', '/GTright/'] rgb_dir = [ self.dataset_dir + sequence_f + rgb_f for rgb_f in rgb_folder for sequence_f in sequence_folder ] rgb_data = [ rgb_d + rgb for rgb_d in rgb_dir for rgb in os.listdir(rgb_d) ] depth_dir = [ self.dataset_dir + sequence_f + depth_f for depth_f in depth_folder for sequence_f in sequence_folder ] depth_data = [ depth_d + depth for depth_d in depth_dir for depth in os.listdir(depth_d) ] segmentation_dir = [ self.dataset_dir + sequence_f + segmentation_f for segmentation_f in segmentation_folder for sequence_f in sequence_folder ] segmentation_data = [ segmentation_d + segmentation for segmentation_d in segmentation_dir for segmentation in os.listdir(segmentation_d) ] dataset = { 'RGB': rgb_data, 'DEPTH': depth_data, 'SEGMENTATION': segmentation_data } if self.shuffle: return pd.DataFrame(dataset).sample(frac=1, random_state=123) return pd.DataFrame(dataset) def next(self): """Retrieve the next pairs from the dataset""" if self.index + self.batch_size > self.end_index: if not self.repeater: raise StopIteration else: self.index = self.start_index self.index = self.index + self.batch_size features = np.array([ resize(image=imread(self.dataset.iloc[i, 0])[:, :, :3], output_shape=self.output_shape) for i in range(self.index - self.batch_size, self.index) ]) features = np.array(features, dtype=np.float32) if self.label_type == 'depth': labels = np.array([ resize(image=imread(self.dataset.iloc[i, 1]), output_shape=self.output_shape) for i in range(self.index, self.index + self.batch_size) ]) labels = img_as_ubyte(labels) labels = np.array(labels, dtype=np.float) labels = (labels[:, :, :, 0] + labels[:, :, :, 1] * 256 + labels[:, :, :, 2] * 256 * 256) / ( (256 * 256 * 256) - 1) * self.max_distance elif self.label_type == 'segmentation': labels = np.array([ resize(image=imread(self.dataset.iloc[i, 2])[:, :, 0], output_shape=self.output_shape) for i in range(self.index - self.batch_size, self.index) ]) labels = img_as_ubyte(labels) new_segmentation = np.ndarray(shape=(self.batch_size, 480, 640, 22)) for i in range(self.batch_size): for j in range(480): for k in range(640): if labels[i, j, k] < 22: new_segmentation[i, j, k, int(labels[i, j, k])] = 1 labels = new_segmentation elif self.label_type == 'sparse_segmentation': labels = np.array([ resize(image=imread(self.dataset.iloc[i, 2])[:, :, 0], output_shape=self.output_shape + (1, )) for i in range(self.index - self.batch_size, self.index) ]) labels = img_as_ubyte(labels) else: raise ValueError('invalid label type') return features, labels class NYU: """Iterator for looping over elements of NYU Depth Dataset V2 backwards.""" def __init__(self, NYU_Depth_Dataset_V2_address, batch_size=1, repeater=False, label_type='segmentation'): self.file = tables.open_file(NYU_Depth_Dataset_V2_address) self.index = len(self.file.root.images) - 1 self.batch_size = batch_size self.repeater = repeater if isinstance(label_type, str): self.label_type = label_type else: self.label_type = label_type.decode() def __iter__(self): return self # Python 3 compatibility def __next__(self): return self.next() def next(self): """Retrieve the next pairs from the dataset """ if self.index - self.batch_size <= 0: if not self.repeater: raise StopIteration else: self.index = len(self.file.root.images) - 1 self.index = self.index - self.batch_size features = np.array(self.file.root.images[self.index:self.index + self.batch_size]) features = np.transpose(features, [0, 3, 2, 1]) features = np.array(features / 255., dtype=np.float16) if self.label_type == 'depth': label_set = self.file.root.depths labels = np.array(label_set[self.index:self.index + self.batch_size]) labels = np.transpose(labels, [0, 2, 1]) labels = np.reshape( labels, (labels.shape[0], labels.shape[1], labels.shape[2], 1)) elif self.label_type == 'segmentation': label_set = self.file.root.labels labels = np.array(label_set[self.index:self.index + self.batch_size]) labels = np.transpose(labels, [0, 2, 1]) labels = np.reshape( labels, (labels.shape[0], labels.shape[1], labels.shape[2], 1)) new_segmentation = np.ndarray(shape=(self.batch_size, 480, 640, 21)) for i in range(self.batch_size): for j in range(480): for k in range(640): if labels[i, j, k] < 21: new_segmentation[i, j, k, int(labels[i, j, k])] = 1 labels = new_segmentation labels = np.array(labels, dtype=np.float16) else: raise ValueError('invalid label type') return features, labels class SynthiaSf: """Iterator for looping over elements of SYNTHIA-SF backwards.""" def __init__(self, synthia_sf_address, batch_size=1, repeater=False, label_type='segmentation', shuffle=False): self.dataset_address = synthia_sf_address self.sequence_folder = [ '/SEQ1', '/SEQ2', '/SEQ3', '/SEQ4', '/SEQ5', '/SEQ6' ] self.rgb_folder = ['/RGBLeft/', '/RGBRight/'] self.depth_folder = ['/DepthLeft/', '/DepthRight/'] self.segmentation_folder = ['/GTLeft/', '/GTright/'] self.batch_size = batch_size self.repeater = repeater self.label_type = label_type self.shuffle = shuffle self.dataset = self.data_frame_creator() self.index = self.dataset.shape[0] - 1 self.max_distance = 1000 def data_frame_creator(self): """ pandas dataFrame for addresses of rgb, depth and segmentation""" rgb_dir = [ self.dataset_address + sequence_f + rgb_f for rgb_f in self.rgb_folder for sequence_f in self.sequence_folder ] rgb_data = [ rgb_d + rgb for rgb_d in rgb_dir for rgb in os.listdir(rgb_d) ] depth_dir = [ self.dataset_address + sequence_f + depth_f for depth_f in self.depth_folder for sequence_f in self.sequence_folder ] depth_data = [ depth_d + depth for depth_d in depth_dir for depth in os.listdir(depth_d) ] segmentation_dir = [ self.dataset_address + sequence_f + segmentation_f for segmentation_f in self.segmentation_folder for sequence_f in self.sequence_folder ] segmentation_data = [ segmentation_d + segmentation for segmentation_d in segmentation_dir for segmentation in os.listdir(segmentation_d) ] dataset = { 'RGB': rgb_data, 'DEPTH': depth_data, 'SEGMENTATION': segmentation_data } if self.shuffle: return pd.DataFrame(dataset).sample(frac=1) return pd.DataFrame(dataset) def __iter__(self): return self # Python 3 compatibility def __next__(self): return self.next() def next(self): """Retrieve the next pairs from the dataset""" if self.index - self.batch_size <= 0: if not self.repeater: raise StopIteration else: self.index = self.dataset.shape[0] - 1 self.index = self.index - self.batch_size features = np.array([ resize(image=imread(self.dataset.iloc[i, 0])[:, :, :3], output_shape=(480, 640)) for i in range(self.index, self.index + self.batch_size) ]) # features = np.array(features / 255., dtype=np.float32) if self.label_type == 'depth': labels = np.array([ resize(image=imread(self.dataset.iloc[i, 1]), output_shape=(480, 640)) for i in range(self.index, self.index + self.batch_size) ]) labels = img_as_ubyte(labels) labels = np.array(labels, dtype=np.float) labels = (labels[:, :, :, 0] + labels[:, :, :, 1] * 256 + labels[:, :, :, 2] * 256 * 256) / ( (256 * 256 * 256) - 1) * self.max_distance elif self.label_type == 'segmentation': labels = np.array([ resize(image=imread(self.dataset.iloc[i, 2])[:, :, 0], output_shape=(480, 640)) for i in range(self.index, self.index + self.batch_size) ]) labels = img_as_ubyte(labels) new_segmentation = np.ndarray(shape=(self.batch_size, 480, 640, 22)) for i in range(self.batch_size): for j in range(480): for k in range(640): if labels[i, j, k] < 22: new_segmentation[i, j, k, int(labels[i, j, k])] = 1 labels = new_segmentation elif self.label_type == 'sparse_segmentation': labels = np.array([ resize(image=imread(self.dataset.iloc[i, 2])[:, :, 0], output_shape=(480, 640, 1)) for i in range(self.index, self.index + self.batch_size) ]) labels = img_as_ubyte(labels) else: raise ValueError('invalid label type') return features, labels # class VIPER(DatasetGenerator): # """Iterator for looping over elements of a VIPER(PlayForBenchmark) dataset .""" # def init(self): # self.seg_dic = segmentation_dics.VIPER # def data_frame_creator(self): # """Pandas dataFrame for addresses of images and corresponding labels""" # img_dir_list = [ # self.dataset_dir + '/train' + '/img' + '/' + seq_dir + '/' + # img_name # for seq_dir in os.listdir(self.dataset_dir + '/train/' + '/img/') # for img_name in os.listdir(self.dataset_dir + '/train/' + '/img/' + # seq_dir) # ] # cls_dir_list = [ # self.dataset_dir + '/train' + '/cls' + '/' + seq_dir + '/' + # img_name # for seq_dir in os.listdir(self.dataset_dir + '/train/' + '/cls/') # for img_name in os.listdir(self.dataset_dir + '/train/' + '/cls/' + # seq_dir) # ] # inst_dir_list = [ # self.dataset_dir + '/train' + '/inst' + '/' + seq_dir + '/' + # img_name # for seq_dir in os.listdir(self.dataset_dir + '/train/' + '/inst/') # for img_name in os.listdir(self.dataset_dir + '/train/' + # '/inst/' + seq_dir) # ] # dataset = { # 'image': img_dir_list, # 'segmentation': cls_dir_list, # 'instance': inst_dir_list # } # if self.shuffle: # return pd.DataFrame(dataset).sample(frac=1) # return pd.DataFrame(dataset)
from pyalgs.algorithms.commons import util from pyalgs.data_structures.commons.bag import Bag class Graph(object): V = 0 adjList = None def __init__(self, V): self.V = V self.adjList = [None] * V for v in range(V): self.adjList[v] = Bag() def vertex_count(self): return self.V def adj(self, v): return self.adjList[v].iterate() def add_edge(self, v, w): self.adjList[v].add(w) self.adjList[w].add(v) class Digraph(object): V = 0 adjList = None def __init__(self, V): self.V = V self.adjList = [None] * V for v in range(V): self.adjList[v] = Bag() def vertex_count(self): return self.V def adj(self, v): return self.adjList[v].iterate() def add_edge(self, v, w): self.adjList[v].add(w) def reverse(self): g = Digraph(self.V) for v in range(self.V): for w in self.adjList[v].iterate(): g.add_edge(w, v) return g class Edge(object): v = None w = None weight = 0 def __init__(self, v=None, w=None, weight=None): if weight is None: weight = 0 self.v = v self.w = w self.weight = weight def start(self): return self.v def end(self): return self.w def reverse(self): return Edge(self.w, self.v, self.weight) def either(self): return self.v def other(self, v): if self.v == v: return self.w elif self.w == v: return self.v else: raise ValueError('mismatched vertex detected') # use for python 2 comparison def __cmp__(self, other): return util.cmp(self.weight, other.weight) # user for python 3 comparison def __lt__(self, other): return util.less(self.weight, other.weight) # user for python 3 comparison def __gt__(self, other): return util.greater(self.weight, other.weight) def __str__(self): return str(self.v) + ' =(' + str(self.weight) + ')=> ' + str(self.w) class EdgeWeightedGraph(object): adjList = None V = 0 def __init__(self, vertex_count): self.V = vertex_count self.adjList = [None] * vertex_count for v in range(vertex_count): self.adjList[v] = Bag() def add_edge(self, edge): v = edge.either() w = edge.other(v) self.adjList[v].add(edge) self.adjList[w].add(edge) def adj(self, v): return self.adjList[v].iterate() def vertex_count(self): return self.V def edges(self): for v in range(self.V): for e in self.adj(v): if e.either() == v: yield e def to_graph(self): g = Graph() for e in self.edges(): g.add_edge(e.start(), e.end()) return g def to_edge_weighted_digraph(self): g = DirectedEdgeWeightedGraph(self.V) for e in self.edges(): g.add_edge(e) g.add_edge(e.reverse()) return g class DirectedEdgeWeightedGraph(object): adjList = None V = 0 def __init__(self, vertex_count): self.V = vertex_count self.adjList = [None] * vertex_count for v in range(vertex_count): self.adjList[v] = Bag() def add_edge(self, edge): v = edge.start() self.adjList[v].add(edge) def adj(self, v): return self.adjList[v].iterate() def vertex_count(self): return self.V def edges(self): for v in range(self.V): for e in self.adj(v): yield e def to_graph(self): g = Graph() for e in self.edges(): g.add_edge(e.start(), e.end()) return g def to_digraph(self): g = Digraph(self.V) for e in self.edges(): g.add_edge(e.start(), e.end()) return g class FlowEdge(object): v = 0 w = 0 capacity = 0 flow = 0 def __init__(self, v, w, capacity): self.v = v self.w = w self.capacity = capacity def residual_capacity_to(self, end_v): if self.w == end_v: return self.capacity - self.flow elif self.v == end_v: return self.flow else: raise ValueError('end point not belong to flow edge') def add_residual_flow_to(self, end_v, delta): if self.v == end_v: self.flow -= delta elif self.w == end_v: self.flow += delta else: raise ValueError('end point not belong to flow edge') def start(self): return self.v def end(self): return self.w def other(self, x): if x == self.v: return self.w else: return self.v def __str__(self): return str(self.v) + ' =(' + str(self.capacity) + ', ' + str(self.flow) + ')=> ' + str(self.w) class FlowNetwork(object): V = None adjList = None def __init__(self, V): self.V = V self.adjList = [None] * V for v in range(self.V): self.adjList[v] = Bag() def adj(self, v): return self.adjList[v].iterate() def add_edge(self, edge): self.adjList[edge.start()].add(edge) self.adjList[edge.end()].add(edge) def vertex_count(self): return self.V def edges(self): for v in range(self.V): for e in self.adjList[v].iterate(): if e.start() == v: yield e
import json import pytest from bs4 import BeautifulSoup from flask import url_for from freezegun import freeze_time from app.main.views.jobs import get_time_left from tests.conftest import ( SERVICE_ONE_ID, mock_get_notifications, normalize_spaces, ) def test_get_jobs_should_return_list_of_all_real_jobs( logged_in_client, service_one, active_user_with_permissions, mock_get_jobs, mocker, ): response = logged_in_client.get(url_for('main.view_jobs', service_id=service_one['id'])) assert response.status_code == 200 page = BeautifulSoup(response.data.decode('utf-8'), 'html.parser') assert page.h1.string == 'Uploaded files' jobs = [x.text for x in page.tbody.find_all('a', {'class': 'file-list-filename'})] assert len(jobs) == 4 def test_get_jobs_shows_page_links( logged_in_client, service_one, active_user_with_permissions, mock_get_jobs, mocker, ): response = logged_in_client.get(url_for('main.view_jobs', service_id=service_one['id'])) assert response.status_code == 200 page = BeautifulSoup(response.data.decode('utf-8'), 'html.parser') assert 'Next page' in page.find('li', {'class': 'next-page'}).text assert 'Previous page' in page.find('li', {'class': 'previous-page'}).text @pytest.mark.parametrize( "status_argument, expected_api_call", [ ( '', [ 'created', 'pending', 'sending', 'pending-virus-check', 'delivered', 'sent', 'failed', 'temporary-failure', 'permanent-failure', 'technical-failure', 'virus-scan-failed', ] ), ( 'sending', ['sending', 'created', 'pending', 'pending-virus-check'] ), ( 'delivered', ['delivered', 'sent'] ), ( 'failed', ['failed', 'temporary-failure', 'permanent-failure', 'technical-failure', 'virus-scan-failed'] ) ] ) @freeze_time("2016-01-01 11:09:00.061258") def test_should_show_page_for_one_job( logged_in_client, service_one, active_user_with_permissions, mock_get_service_template, mock_get_job, mocker, mock_get_notifications, fake_uuid, status_argument, expected_api_call, ): response = logged_in_client.get(url_for( 'main.view_job', service_id=service_one['id'], job_id=fake_uuid, status=status_argument )) assert response.status_code == 200 page = BeautifulSoup(response.data.decode('utf-8'), 'html.parser') assert page.h1.text.strip() == 'thisisatest.csv' assert ' '.join(page.find('tbody').find('tr').text.split()) == ( '07123456789 template content Delivered 1 January at 11:10am' ) assert page.find('div', {'data-key': 'notifications'})['data-resource'] == url_for( 'main.view_job_updates', service_id=service_one['id'], job_id=fake_uuid, status=status_argument, ) csv_link = page.select_one('a[download]') assert csv_link['href'] == url_for( 'main.view_job_csv', service_id=service_one['id'], job_id=fake_uuid, status=status_argument ) assert csv_link.text == 'Download this report' assert page.find('span', {'id': 'time-left'}).text == 'Data available for 7 days' mock_get_notifications.assert_called_with( service_one['id'], fake_uuid, status=expected_api_call ) def test_get_jobs_should_tell_user_if_more_than_one_page( logged_in_client, fake_uuid, service_one, mock_get_job, mock_get_service_template, mock_get_notifications_with_previous_next, ): response = logged_in_client.get(url_for( 'main.view_job', service_id=service_one['id'], job_id=fake_uuid, status='' )) assert response.status_code == 200 page = BeautifulSoup(response.data.decode('utf-8'), 'html.parser') assert page.find('p', {'class': 'table-show-more-link'}).text.strip() == 'Only showing the first 50 rows' def test_should_show_job_in_progress( logged_in_client, service_one, active_user_with_permissions, mock_get_service_template, mock_get_job_in_progress, mocker, mock_get_notifications, fake_uuid, ): response = logged_in_client.get(url_for( 'main.view_job', service_id=service_one['id'], job_id=fake_uuid )) assert response.status_code == 200 page = BeautifulSoup(response.data.decode('utf-8'), 'html.parser') assert page.find('p', {'class': 'hint'}).text.strip() == 'Report is 50% complete…' @freeze_time("2016-01-01 11:09:00.061258") def test_should_show_letter_job( client_request, mock_get_service_letter_template, mock_get_job, fake_uuid, active_user_with_permissions, mocker, ): get_notifications = mock_get_notifications(mocker, active_user_with_permissions, diff_template_type='letter') page = client_request.get( 'main.view_job', service_id=SERVICE_ONE_ID, job_id=fake_uuid, ) assert normalize_spaces(page.h1.text) == 'thisisatest.csv' assert normalize_spaces(page.select('p.bottom-gutter')[0].text) == ( 'Sent by Test User on 1 January at 11:09am' ) assert page.select('.banner-default-with-tick') == [] assert normalize_spaces(page.select('tbody tr')[0].text) == ( '1 Example Street template content 1 January at 11:09am' ) assert normalize_spaces(page.select('.keyline-block')[0].text) == ( '1 Letter' ) assert normalize_spaces(page.select('.keyline-block')[1].text) == ( '6 January Estimated delivery date' ) assert page.select('[download=download]') == [] assert page.select('.hint') == [] get_notifications.assert_called_with( SERVICE_ONE_ID, fake_uuid, status=[ 'created', 'pending', 'sending', 'pending-virus-check', 'delivered', 'sent', 'failed', 'temporary-failure', 'permanent-failure', 'technical-failure', 'virus-scan-failed', ], ) @freeze_time("2016-01-01 11:09:00.061258") def test_should_show_letter_job_with_banner_after_sending( client_request, mock_get_service_letter_template, mock_get_job, mock_get_notifications, fake_uuid, ): page = client_request.get( 'main.view_job', service_id=SERVICE_ONE_ID, job_id=fake_uuid, just_sent='yes', ) assert page.select('p.bottom-gutter') == [] assert normalize_spaces(page.select('.banner-default-with-tick')[0].text) == ( 'We’ve started printing your letters' ) @freeze_time("2016-01-01T00:00:00.061258") def test_should_show_scheduled_job( logged_in_client, active_user_with_permissions, mock_get_service_template, mock_get_scheduled_job, mocker, mock_get_notifications, fake_uuid, ): response = logged_in_client.get(url_for( 'main.view_job', service_id=SERVICE_ONE_ID, job_id=fake_uuid )) assert response.status_code == 200 page = BeautifulSoup(response.data.decode('utf-8'), 'html.parser') assert normalize_spaces(page.select('main p')[1].text) == ( 'Sending Two week reminder today at midnight' ) assert page.select('main p a')[0]['href'] == url_for( 'main.view_template_version', service_id=SERVICE_ONE_ID, template_id='5d729fbd-239c-44ab-b498-75a985f3198f', version=1, ) assert page.select_one('button[type=submit]').text.strip() == 'Cancel sending' def test_should_cancel_job( logged_in_client, service_one, fake_uuid, mocker, ): mock_cancel = mocker.patch('app.main.jobs.job_api_client.cancel_job') response = logged_in_client.post(url_for( 'main.cancel_job', service_id=service_one['id'], job_id=fake_uuid )) mock_cancel.assert_called_once_with(service_one['id'], fake_uuid) assert response.status_code == 302 assert response.location == url_for('main.service_dashboard', service_id=service_one['id'], _external=True) def test_should_not_show_cancelled_job( logged_in_client, service_one, active_user_with_permissions, mock_get_cancelled_job, mocker, fake_uuid, ): response = logged_in_client.get(url_for( 'main.view_job', service_id=service_one['id'], job_id=fake_uuid )) assert response.status_code == 404 @freeze_time("2016-01-01 00:00:00.000001") def test_should_show_updates_for_one_job_as_json( logged_in_client, service_one, active_user_with_permissions, mock_get_notifications, mock_get_service_template, mock_get_job, mocker, fake_uuid, ): response = logged_in_client.get(url_for('main.view_job_updates', service_id=service_one['id'], job_id=fake_uuid)) assert response.status_code == 200 content = json.loads(response.get_data(as_text=True)) assert 'sending' in content['counts'] assert 'delivered' in content['counts'] assert 'failed' in content['counts'] assert 'Recipient' in content['notifications'] assert '07123456789' in content['notifications'] assert 'Status' in content['notifications'] assert 'Delivered' in content['notifications'] assert '12:01am' in content['notifications'] assert 'Sent by Test User on 1 January at midnight' in content['status'] @pytest.mark.parametrize( "job_created_at, expected_message", [ ("2016-01-10 11:09:00.000000+00:00", "Data available for 7 days"), ("2016-01-04 11:09:00.000000+00:00", "Data available for 1 day"), ("2016-01-03 11:09:00.000000+00:00", "Data available for 11 hours"), ("2016-01-02 23:59:59.000000+00:00", "Data no longer available") ] ) @freeze_time("2016-01-10 12:00:00.000000") def test_time_left(job_created_at, expected_message): assert get_time_left(job_created_at) == expected_message
# Copyright (C) 2015-2016 Regents of the University of California # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import import os import random import socket import signal import sys import threading import pickle import logging import subprocess import traceback from time import sleep, time import psutil import mesos.interface from bd2k.util.expando import Expando from mesos.interface import mesos_pb2 import mesos.native from struct import pack from toil.batchSystems.abstractBatchSystem import BatchSystemSupport from toil.resource import Resource log = logging.getLogger(__name__) class MesosExecutor(mesos.interface.Executor): """ Part of Toil's Mesos framework, runs on a Mesos slave. A Toil job is passed to it via the task.data field, and launched via call(toil.command). """ def __init__(self): super(MesosExecutor, self).__init__() self.popenLock = threading.Lock() self.runningTasks = {} self.workerCleanupInfo = None Resource.prepareSystem() self.address = None # Setting this value at this point will ensure that the toil workflow directory will go to # the mesos sandbox if the user hasn't specified --workDir on the command line. if not os.getenv('TOIL_WORKDIR'): os.environ['TOIL_WORKDIR'] = os.getcwd() def registered(self, driver, executorInfo, frameworkInfo, slaveInfo): """ Invoked once the executor driver has been able to successfully connect with Mesos. """ log.debug("Registered with framework") self.address = socket.gethostbyname(slaveInfo.hostname) nodeInfoThread = threading.Thread(target=self._sendFrameworkMessage, args=[driver]) nodeInfoThread.daemon = True nodeInfoThread.start() def reregistered(self, driver, slaveInfo): """ Invoked when the executor re-registers with a restarted slave. """ log.debug("Re-registered") def disconnected(self, driver): """ Invoked when the executor becomes "disconnected" from the slave (e.g., the slave is being restarted due to an upgrade). """ log.critical("Disconnected from slave") def killTask(self, driver, taskId): """ Kill parent task process and all its spawned children """ try: pid = self.runningTasks[taskId] pgid = os.getpgid(pid) except KeyError: pass else: os.killpg(pgid, signal.SIGKILL) def shutdown(self, driver): log.critical('Shutting down executor ...') for taskId in self.runningTasks.keys(): self.killTask(driver, taskId) Resource.cleanSystem() BatchSystemSupport.workerCleanup(self.workerCleanupInfo) log.critical('... executor shut down.') def error(self, driver, message): """ Invoked when a fatal error has occurred with the executor and/or executor driver. """ log.critical("FATAL ERROR: " + message) def _sendFrameworkMessage(self, driver): message = None while True: # The psutil documentation recommends that we ignore the value returned by the first # invocation of cpu_percent(). However, we do want to send a sign of life early after # starting (e.g. to unblock the provisioner waiting for an instance to come up) so # the first message we send omits the load info. if message is None: message = Expando(address=self.address) psutil.cpu_percent() else: message.nodeInfo = dict(coresUsed=float(psutil.cpu_percent()) * .01, memoryUsed=float(psutil.virtual_memory().percent) * .01, coresTotal=psutil.cpu_count(), memoryTotal=psutil.virtual_memory().total, workers=len(self.runningTasks)) driver.sendFrameworkMessage(repr(message)) # Prevent workers launched together from repeatedly hitting the leader at the same time sleep(random.randint(45, 75)) def launchTask(self, driver, task): """ Invoked by SchedulerDriver when a Mesos task should be launched by this executor """ def runTask(): log.debug("Running task %s", task.task_id.value) sendUpdate(mesos_pb2.TASK_RUNNING) try: taskData = pickle.loads(task.data) except: exc_info = sys.exc_info() log.error('Exception while unpickling task:', exc_info=exc_info) exc_type, exc_value, exc_trace = exc_info sendUpdate(mesos_pb2.TASK_FAILED, wallTime=None, message=''.join(traceback.format_exception_only(exc_type, exc_value))) return # This is where task.data is first invoked. Using this position to setup cleanupInfo if self.workerCleanupInfo is not None: assert self.workerCleanupInfo == taskData.workerCleanupInfo else: self.workerCleanupInfo = taskData.workerCleanupInfo startTime = time() try: popen = runJob(taskData) self.runningTasks[task.task_id.value] = popen.pid try: exitStatus = popen.wait() wallTime = time() - startTime if 0 == exitStatus: sendUpdate(mesos_pb2.TASK_FINISHED, wallTime) elif -9 == exitStatus: sendUpdate(mesos_pb2.TASK_KILLED, wallTime) else: sendUpdate(mesos_pb2.TASK_FAILED, wallTime, message=str(exitStatus)) finally: del self.runningTasks[task.task_id.value] except: wallTime = time() - startTime exc_info = sys.exc_info() log.error('Exception while running task:', exc_info=exc_info) exc_type, exc_value, exc_trace = exc_info sendUpdate(mesos_pb2.TASK_FAILED, wallTime, message=''.join(traceback.format_exception_only(exc_type, exc_value))) def runJob(job): """ :type job: toil.batchSystems.mesos.ToilJob :rtype: subprocess.Popen """ if job.userScript: job.userScript.register() log.debug("Invoking command: '%s'", job.command) with self.popenLock: return subprocess.Popen(job.command, preexec_fn=lambda: os.setpgrp(), shell=True, env=dict(os.environ, **job.environment)) def sendUpdate(taskState, wallTime=None, message=''): log.debug('Sending task status update ...') status = mesos_pb2.TaskStatus() status.task_id.value = task.task_id.value status.message = message status.state = taskState if wallTime is not None: status.data = pack('d', wallTime) driver.sendStatusUpdate(status) log.debug('... done sending task status update.') thread = threading.Thread(target=runTask) thread.start() def frameworkMessage(self, driver, message): """ Invoked when a framework message has arrived for this executor. """ log.debug("Received message from framework: {}".format(message)) def main(executorClass=MesosExecutor): logging.basicConfig(level=logging.DEBUG) log.debug("Starting executor") executor = executorClass() driver = mesos.native.MesosExecutorDriver(executor) exit_value = 0 if driver.run() == mesos_pb2.DRIVER_STOPPED else 1 assert len(executor.runningTasks) == 0 sys.exit(exit_value)
from __future__ import print_function, absolute_import import os from qtpy import QtWidgets, QtCore from fem.base_app.configuration import BaseConfiguration from fem.base_app.model import BaseModel from fem.utilities import BaseObject from .base_file_menu import BaseFileMenu from .base_edit_menu import BaseEditMenu from .base_view_menu import BaseViewMenu from .base_help_menu import BaseHelpMenu from .base_beta_menu import BaseBetaMenu from .base_logging_dock import BaseLoggingDock class BaseMainWindow(QtWidgets.QMainWindow, BaseObject): BaseConfiguration = BaseConfiguration BaseModel = BaseModel BaseFileMenu = BaseFileMenu BaseEditMenu = BaseEditMenu BaseViewMenu = BaseViewMenu BaseHelpMenu = BaseHelpMenu BaseBetaMenu = BaseBetaMenu BaseLoggingDock = BaseLoggingDock def __init__(self, *args): super(BaseMainWindow, self).__init__(*args) self._main_data = self.BaseModel.instance() self.config = self.BaseConfiguration.instance() self.config.register_main_window(self) self.config.register_main_data(self._main_data) self.logging_dock = self.BaseLoggingDock.instance(self) self.addDockWidget(QtCore.Qt.BottomDockWidgetArea, self.logging_dock) self.file_menu = self.BaseFileMenu.instance(self) self.edit_menu = self.BaseEditMenu.instance(self) self.view_menu = self.BaseViewMenu.instance(self) self.help_menu = self.BaseHelpMenu.instance(self) self.beta_menu = self.BaseBetaMenu.instance(self) self.config.dispatcher.undo_stack.cleanChanged.connect(self._clean_changed) self.update_window_title() def log_text(self): return self.logging_dock.log_text() def save_settings(self): settings_file = self.config.settings_file() if settings_file is None: return settings = QtCore.QSettings(settings_file, QtCore.QSettings.IniFormat) settings.setValue('geometry', self.saveGeometry()) settings.setValue('windowState', self.saveState()) def read_settings(self): settings_file = self.config.settings_file() if settings_file is None or not os.path.isfile(settings_file): settings_file = self.config.default_settings_file() settings = QtCore.QSettings(settings_file, QtCore.QSettings.IniFormat) self.restoreGeometry(settings.value("geometry")) self.restoreState(settings.value("windowState")) def main_data(self): return self._main_data def update_all(self): raise NotImplementedError def update_window_title(self): self.setWindowTitle(self.config.window_title()) def sizeHint(self): return QtCore.QSize(1800, 1200) def closeEvent(self, event, *args, **kwargs): if not self.file_menu.check_on_close(): event.ignore() return self._before_close() super(BaseMainWindow, self).closeEvent(event, *args, **kwargs) def _before_close(self): self.save_settings() def _clean_changed(self, is_clean): self.update_window_title() @classmethod def copy_cls(cls): """ :return: :rtype: BaseMainWindow """ # noinspection PyAbstractClass class _Tmp(cls): BaseConfiguration = BaseConfiguration.copy_cls() BaseMainData = BaseModel.copy_cls() BaseFileMenu = BaseFileMenu.copy_cls() BaseEditMenu = BaseEditMenu.copy_cls() BaseViewMenu = BaseViewMenu.copy_cls() BaseHelpMenu = BaseHelpMenu.copy_cls() BaseBetaMenu = BaseBetaMenu.copy_cls() BaseLoggingDock = BaseLoggingDock.copy_cls() _Tmp.BaseFileMenu.BaseConfiguration = _Tmp.BaseConfiguration _Tmp.BaseEditMenu.BaseConfiguration = _Tmp.BaseConfiguration _Tmp.BaseViewMenu.BaseConfiguration = _Tmp.BaseConfiguration _Tmp.BaseHelpMenu.BaseConfiguration = _Tmp.BaseConfiguration _Tmp.BaseBetaMenu.BaseConfiguration = _Tmp.BaseConfiguration _Tmp.BaseLoggingDock.BaseConfiguration = _Tmp.BaseConfiguration _Tmp.__name__ = cls.__name__ return _Tmp
<reponame>josephch405/airdialogue<filename>airdialogue/context_generator/src/utils.py # Copyright 2019 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """this file contains a list of utility functions.""" import calendar from datetime import datetime import numpy as np def continue_booking(facts, ag_cond, kb, goal_str): flights = airflight_selector(facts, ag_cond, kb) # this is a list of flights if not flights: # terminal condition status = 'no_flight' else: status = goal_str return status, flights def airflight_selector(facts, condition, airfare_database): """This function selects a flight based on the condition.""" candidates = [] for i, flight in enumerate(airfare_database): cond = check_condition(facts, flight, condition, get_full_diff=False) if cond == 'satisfied': candidates.append(i) candidates.sort(key=lambda a: airfare_database[a]['price']) if not candidates: return None else: upper = 0 flight = airfare_database[candidates[upper]] val = flight['price'] all_good_flights = [flight] upper += 1 # find all flights with the same price while upper < len(candidates): flight = airfare_database[candidates[upper]] if flight['price'] == val: all_good_flights.append(flight) upper += 1 else: break return all_good_flights def generate_expected_action(facts, agent_condition, airfare_database, reservation): """this function generates the expected action based context.""" goal_str = agent_condition['goal'] if goal_str == 'book' or (goal_str == 'change' and reservation != 0): status, flights = continue_booking(facts, agent_condition, airfare_database, goal_str) elif goal_str in ['change', 'cancel'] and reservation == 0: status = 'no_reservation' flights = [] elif goal_str == 'cancel': status = 'cancel' flights = [] expected_action = generate_action(flights, agent_condition['name'], status) return expected_action def check_states(user_action, expected_action): """this function check the dialogue states of the json_object. assume both expected action and user action went through action standarlization, which means they must have a flight and name field. """ # first status needs to match status_match = expected_action['status'] == user_action['status'] # second flight need to match # if user flight is empty, expected flight has to be empty if len(user_action['flight']) == 0: flight_match = len(expected_action['flight']) == 0 else: # there can only be one user flight assert len(user_action['flight']) == 1 flight_match = user_action['flight'][0] in expected_action['flight'] a = expected_action['name'].strip().lower() b = user_action['name'].strip().lower() name_match = (a == b) res = status_match and flight_match and name_match return res, status_match, flight_match, name_match def get_day_segment_by_hour(hour): if hour < 3 or hour > 19: return 'evening' elif hour >= 3 and hour <= 11: return 'morning' else: return 'afternoon' def check_condition(facts, flight, condition, get_full_diff=False): """Check the condition of the fliths to see whether it is satisfied.""" # all the keys here have been confirmed to appear in step2 of data release. diff = [] do_not_consider = set(['name', 'goal', 'departure_date', 'return_date']) for key in set(condition.keys()) - do_not_consider: if key == 'departure_time': day_segment = get_day_segment_by_hour(flight['departure_time_num']) if day_segment != condition[key]: diff.append(key) elif key == 'return_time': day_segment = get_day_segment_by_hour(flight['return_time_num']) if day_segment != condition[key]: diff.append(key) elif key == 'airline_preference': if condition[key] != facts.airline_list[flight['airline']]: diff.append(key) elif key == 'max_connections': if condition[key] < flight['num_connections']: # print ('ss',condition[key],flight['num_connections']) diff.append(key) elif key == 'max_price': if condition[key] < flight['price']: diff.append(key) else: # this includes, class, departure_airport/return_airport # departure_month/return_month, departure_day/return_day if flight[key] != condition[key]: diff.append(key) if not get_full_diff and len(diff): return diff if not diff: return 'satisfied' else: return diff def generate_action(flights, name, status): """generate dialogue action.""" action_json = {} if status.startswith('no_flight'): status = 'no_flight' action_json['status'] = status flight_lst = [] # always have an empty flight list if status in ['change', 'book']: for f in flights: flight_lst.append(f['flight_number']) action_json['flight'] = flight_lst action_json['name'] = name # standarlize it just in case standarlized_action = standardize_action(action_json) return standarlized_action def standardize_intent(org_intent): """1) get ride of departure_date and return_date in the intent. 2) also get tide of intents with 'all'. also for max_conn if val is 2. 3) make goal a string. 4) replace '_' with ' ' in name. """ new_intent = {} for key in org_intent: if key in ['departure_date', 'return_date']: continue if org_intent[key] == 'all': continue if key == 'max_connections' and org_intent[key] == 2: continue new_intent[key] = org_intent[key] new_intent['goal'] = ['book', 'change', 'cancel'][new_intent['goal']] new_intent['name'] = new_intent['name'].replace('_', ' ').strip() return new_intent def standardize_action(org_action): """if status is not book or change the flight number will be empty. name is always required.""" # some human raters will end a name with . or , # since names in intent are standarlized (with - being replaced by space), # it will not be necessary to consider - again in the action standarlization. original_name = org_action['name'].strip() name = [] for d in original_name: if d.isalpha() or d == ' ': name.append(d) name = ''.join(name) status = org_action['status'] # if flight is a single int, we will need to convert it to a list # ground truth can have multiple flights # prediction and real data have no more than one element in the flight list. flight = org_action['flight'] if type(flight) == int: flight = [flight] if status == 'book' or status == 'change': new_flight = [] # get ride of anything that is not a valid flight number. # This could be the empty flights in early version of the UI. for f in flight: if int(f) >= 1000: new_flight.append(f) else: # otherwise we provide an empty list of the flight # any user selecged flights that does not come with bookable status # will be ignored. new_flight = [] return {'flight': new_flight, 'name': name, 'status': status} def get_unix_epoch(dt): return calendar.timegm(dt.utctimetuple()) def get_datetime(unix_epoch): return datetime.utcfromtimestamp(unix_epoch) def get_month_and_day(fact_obj, unix_epoch): m = get_datetime(unix_epoch).month d = get_datetime(unix_epoch).day month = fact_obj.months[m - 1] return month, str(d) def get_hour(unix_epoch): return get_datetime(unix_epoch).hour def discrete_price(original_price): return max(100, int(original_price / 100) * 100) def format_time(hour): return str(hour) + ':00 in the ' + get_day_segment_by_hour(hour) def get_connection(con): if con == 0: return 'direct service' elif con == 1: return '1 connection' else: return '2 connections' def discrete_sample_(probabilities): if abs(sum(probabilities) - 1) > 1e-3: raise ValueError('sum of probability not equal to 1') sm = 0.0 random_number = np.random.random() for i in range(len(probabilities)): sm += probabilities[i] if sm >= random_number: return i assert False, 'invalid path' def choice(values, cnt=-1, p=None): if p is None: p = [float(1) / float(len(values))] * len(values) arr = [] for _ in range(abs(cnt)): ind = discrete_sample_(p) arr.append(values[ind]) if cnt == -1: return arr[0] else: return arr
#!/usr/bin/python # -*- coding: utf-8 -*- import os import web import json import subprocess import auth def get(): u,a=auth._auth() if not a:return 403 ds={'disks':_get_disks(),'raids':_get_raids()} return ds def erase(): u,a=auth._auth() if not a:return 403 rq=web.input() p=rq['path'] return _erase(p) def mkpart(): u,a=auth._auth() if not a:return 403 rq=web.input() p=rq['path'] t=rq['type'] s=rq['start'] e=rq['end'] return _mkpart(p,t,s,e) def rmpart(): u,a=auth._auth() if not a:return 403 rq=web.input() p=rq['path'] n=rq['number'] return _rmpart(p,n) def mkfs(): u,a=auth._auth() if not a:return 403 rq=web.input() p=rq['path'] return _mkfs(p) def mount(): u,a=auth._auth() if not a:return 403 rq=web.input() p=rq['path'] return _mount(p) def umount(): u,a=auth._auth() if not a:return 403 rq=web.input() p=rq['path'] return _umount(p) def eject(): u,a=auth._auth() if not a:return 403 rq=web.input() p=rq['path'] return _eject(p) def craid(): u,a=auth._auth() if not a:return 403 rq=web.input() p1=rq['path1'] p2=rq['path2'] return _craid(p1,p2) def draid(): u,a=auth._auth() if not a:return 403 rq=web.input() p=rq['path'] return _draid(p) def rmraid(): u,a=auth._auth() if not a:return 403 rq=web.input() p=rq['path'] d=rq['dev'] return _rmraid(p,d) def addraid(): u,a=auth._auth() if not a:return 403 rq=web.input() p=rq['path'] d=rq['dev'] return _addraid(p,d) def _erase(p): _umount(p) os.system('sudo dmsetup remove_all') c='sudo parted %s mklabel gpt -s'%p r=subprocess.Popen(c,shell=True,stdout=subprocess.PIPE).stdout.read() return {'result':r} def _mkpart(p,t,s,e): c='sudo parted -a cylinder %s mkpart %s %ss %ss -s'%(p,t,s,e) r=subprocess.Popen(c,shell=True,stdout=subprocess.PIPE).stdout.read() return {'result':r} def _rmpart(p,n): u=_get_udev(p)['DEVTYPE'] if u=='disk':return _erase(p) _umount(p) os.system('sudo dmsetup remove_all') c='sudo parted %s rm %s -s'%(p,n) r=subprocess.Popen(c,shell=True,stdout=subprocess.PIPE).stdout.read() return {'result':r} def _mkfs(p): _umount(p) os.system('sudo dmsetup remove_all') c='sudo mkfs.ext4 -F %s'%p r=subprocess.Popen(c,shell=True,stdout=subprocess.PIPE).stdout.read() if 'Filesystem UUID: ' in r: _mount(p) return {'result':r} return {status:0,'message':r} def _mount(p): u=_get_udev(p)['ID_FS_UUID'] os.system('sudo /box/bin/mount block add %s'%u) return 200 def _umount(p): u=_get_udev(p) if u.has_key('ID_FS_UUID'):u=u['ID_FS_UUID'] else:return 200 os.system('sudo /bin/umount -Alf %s'%p) #os.system('sudo /box/bin/mount block remove %s'%u) return 200 def _eject(p): c='sudo eject %s'%p r=subprocess.Popen(c,shell=True,stdout=subprocess.PIPE).stdout.read() return {'result':r} def _craid(p1,p2): i=0 while os.path.exists('/dev/md%s'%i):i+=1 c='sudo echo y | sudo mdadm -C /dev/md%s -a yes -l1 -n2 %s %s'%(i,p1,p2) r=subprocess.Popen(c,shell=True,stdout=subprocess.PIPE).stdout.read() return {'result':r} def _draid(p): _umount(p) ds=_get_raid(p)['devices'] c=['sudo mdadm -S %s'%p] for d in ds: if d.has_key('path'):c.append('sudo mdadm --zero-superblock %s'%d['path']) for l in c:os.system(l) return 200 def _rmraid(p,d): _umount(p) os.system('sudo mdadm %s -f %s -r %s'%(p,d,d)) c='sudo mdadm --zero-superblock %s'%d r=subprocess.Popen(c,shell=True,stdout=subprocess.PIPE).stdout.read() return {'result':r} def _addraid(p,d): c='sudo mdadm %s --add %s'%(p,d) r=subprocess.Popen(c,shell=True,stdout=subprocess.PIPE).stdout.read() return {'result':r} def _get_disks(): c='sudo parted -ls |grep "^Disk /dev"' r=subprocess.Popen(c,shell=True,stdout=subprocess.PIPE).stdout.read() ls=r.split('\n') ds={} for l in ls: if not l.startswith('Disk /dev/'):continue d=l.replace('Disk ','').split(':')[0] ds[d]={'name':d,'udev':_get_udev(d)} c='sudo parted %s -ms u s p free'%d r=subprocess.Popen(c,shell=True,stdout=subprocess.PIPE) _ls=r.stdout.readlines() f=0 for i in _ls: print i _l=i.strip(';\n').split(':') if 'Read-only file system' in i: ds[d]['readonly']=True if i.startswith(d): ds[d]['size']=_l[1].replace('s','') ds[d]['bus']=_l[2] ds[d]['sectorsize']={'logical':_l[3],'physical':_l[4]} ds[d]['parttiontype']=_l[5] ds[d]['model']=_l[6] ds[d]['flags']=_l[7] ds[d]['children']={} if ds[d]['parttiontype'] in ['loop','unknown']: _ds=ds[d]['children'][d]={ 'name':d ,'start':0 ,'end':ds[d]['size'] ,'size':ds[d]['size'] ,'udev':_get_udev(d) } f=1 continue if f>0: if ds[d]['parttiontype'] in ['loop','unknown']: _ds=ds[d]['children'][d]={ 'name':d ,'number':_l[0] ,'start':_l[1].replace('s','') ,'end':_l[2].replace('s','') ,'size':_l[3].replace('s','') ,'fstype':_l[4] ,'type':_l[5] ,'flags':_l[6] ,'udev':_get_udev(d) } continue if d.startswith('/dev/md') or d.startswith('/dev/mmcblk'):p='%sp'%d else:p=d print _l if _l[4]=='free': _p='%s/%s'%(p,_l[1].replace('s','')) ds[d]['children'][_p]={ 'name':_p ,'start':_l[1].replace('s','') ,'end':_l[2].replace('s','') ,'size':_l[3].replace('s','') ,'fstype':_l[4] ,'type':'' } else: _p='%s%s'%(p,_l[0]) if _l[4]!='' and _l[5]=='':_l[5]='primary' ds[d]['children'][_p]={ 'name':_p ,'number':_l[0] ,'start':_l[1].replace('s','') ,'end':_l[2].replace('s','') ,'size':_l[3].replace('s','') ,'fstype':_l[4] ,'type':_l[5] ,'flags':_l[6] ,'udev':_get_udev(_p) } c='sudo lsblk -Jfpb' r=subprocess.Popen(c,shell=True,stdout=subprocess.PIPE).stdout.read() b=json.loads(unicode(r,errors='ignore'))['blockdevices'] for i in b: d=i['name'] ds[d]['fstype']=i['fstype'] if i['fstype']=='linux_raid_member': ds[d]['readonly']=True if ds[d]['children'].has_key(d): _ds=ds[d]['children'][d] _ds['fstype']=i['fstype'] _ds['label']=i['label'] _ds['uuid']=i['uuid'] _ds['mountpoint']=i['mountpoint'] if i['mountpoint']: used,avail,percent=_get_usage(i['mountpoint']) try: if os.path.samefile(i['mountpoint'],'/'): _ds['os']=True _ds['readonly']=True ds[d]['readonly']=True if os.path.samefile(i['mountpoint'],'/boot'): _ds['readonly']=True ds[d]['readonly']=True except:pass _ds['used']=used _ds['avail']=avail _ds['percent']=percent if i.has_key('children'): for j in i['children']: n=j['name'] if i['fstype']=='linux_raid_member': if ds.has_key(n): if ds[n]['children'].has_key(n): _ds=ds[n]['children'][n] _dsp=ds[n] else:_ds={} else: _n=n[:n.index('p')] _ds=ds[_n]['children'][n] _dsp=ds[_n] else: if ds[d]['children'].has_key(n): _ds=ds[d]['children'][n] else: ds[d]['children'][n]=j _ds=j _dsp=ds[d] _ds['label']=j['label'] _ds['uuid']=j['uuid'] _ds['mountpoint']=j['mountpoint'] if j['mountpoint']: used,avail,percent=_get_usage(j['mountpoint']) try: if os.path.samefile(j['mountpoint'],'/'): _ds['os']=True _ds['readonly']=True _dsp['readonly']=True if os.path.samefile(j['mountpoint'],'/boot'): _ds['readonly']=True _dsp['readonly']=True except:pass _ds['used']=used _ds['avail']=avail _ds['percent']=percent return ds def _get_usage(d): d=os.path.realpath(d) vfs = os.statvfs(d) avail=vfs.f_bfree*vfs.f_bsize used=(vfs.f_blocks-vfs.f_bfree)*vfs.f_bsize percent=100*(vfs.f_blocks-vfs.f_bfree)/vfs.f_blocks return used,avail,percent def _get_mountpoints(): c='sudo cat /proc/mounts |grep ^/dev'; r=subprocess.Popen(c,shell=True,stdout=subprocess.PIPE).stdout.read() _r=r.split('\n') m={} for i in _r: device,mountpoint,fs,option,cflag,dflag=i.split(' ') if not hasattr(m,device):m[device]=[] m[device].append(mountpoint) return m def _get_raids(): c='sudo mdadm -Ds'; r=subprocess.Popen(c,shell=True,stdout=subprocess.PIPE).stdout.read() _r=r.split('\n') m={} for i in _r: if i=='':continue d=i.split(' ') d=os.path.realpath(d[1]) m[d]=_get_raid(d) m[d]['udev']=_get_udev(d) return m def _get_raid(d): c='sudo mdadm -D %s'%d; r=subprocess.Popen(c,shell=True,stdout=subprocess.PIPE).stdout.read() _r=r.split('\n') result={'devices':[]} for i in _r: i=i.strip() if i=='%s:'%d or i=='' or i.startswith('Number'):continue if ' : ' in i: k,v=i.split(' : ') result[k]=v else: l=' '.join(filter(lambda x: x, i.split(' '))).split(' ') if len(l)==7:result['devices'].append({'path':l[6],'state':l[4],'sync':l[5]}) if len(l)==5:result['devices'].append({'state':l[4]}) return result def _q_raid(d): result=None c='sudo mdadm -Q %s'%d; r=subprocess.Popen(c,shell=True,stdout=subprocess.PIPE).stdout.read() _r=r.split('\n') for i in _r: i=i.replace('. Use mdadm --examine for more detail.','') if '%s: device'%d in i:result=os.path.realpath(i[i.rfind(' ')+1:]) return result def _get_udev(d): udev={} s=['BUSNUM','DEVNUM','DEVNAME','DEVTYPE','ID_BUS','ID_CDROM','ID_MTP_DEVICE','ID_VENDOR','ID_MODEL','ID_PART_TABLE_TYPE','ID_SERIAL_SHORT','ID_FS_LABEL','ID_FS_UUID','ID_FS_TYPE','SUBSYSTEM','ID_USB_DRIVER'] c='sudo udevadm info %s'%d r=subprocess.Popen(c,shell=True,stdout=subprocess.PIPE).stdout.read() for _s in s: i=r.find(_s+'=') if i>0: i=i+len(_s)+1 j=r.find('\n',i) udev[_s]=r[i:j] return udev
<reponame>Chen-yu-Zheng/Email-System """ 文件名：send.py 作者：张钊为 介绍：使用构建的SMTP模块实现邮件到SMTP服务器的发送 创建时间：2021/7/30 """ from module import smtp from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart from email.mime.base import MIMEBase from email import encoders from email.header import Header from email.mime.text import MIMEText from email.utils import parseaddr, formataddr def _format_addr(s): name, addr = parseaddr(s) return formataddr((Header(name, 'utf-8').encode(), addr)) def send2others(from_addr, password, to_addr, message_topic, message, send_file=False, smtp_server= 'smtp.qq.com'): if len(message) >= 6 and message[0:6] == '<html>': # 网页文件 msg = MIMEMultipart('alternative') # # 正常发送msg对象... msg = MIMEText(message, 'html', 'utf-8')#'<html><body><h1>Hello</h1>' + '<p>send by <a href="http://www.python.org">Python</a>...</p>' + '</body></html>', 'html', 'utf-8') else: # 普通邮件 msg = MIMEText(message, 'plain', 'utf-8')#'hello, send by Python...', 'plain', 'utf-8') if send_file is True: send_path = input('file path: ') # 附件 # 邮件对象: msg = MIMEMultipart() # 邮件正文是MIMEText: msg.attach(MIMEText(message, 'plain', 'utf-8')) # 添加附件就是加上一个MIMEBase，从本地读取一个图片: with open(send_path, 'rb') as f: # 设置附件的MIME和文件名: mime = MIMEBase('image', 'png', filename=send_path.split('/')[-1]) # 加上必要的头信息: mime.add_header('Content-Disposition', 'attachment', filename=send_path.split('/')[-1]) mime.add_header('Content-ID', '<0>') mime.add_header('X-Attachment-Id', '0') # 把附件的内容读进来: mime.set_payload(f.read()) # 用Base64编码: encoders.encode_base64(mime) # 添加到MIMEMultipart: msg.attach(mime) send_file = False # msg = MIMEText('hello, send by Python...', 'plain', 'utf-8') msg['From'] = _format_addr(from_addr) msg['To'] = _format_addr(to_addr) msg['Subject'] = Header(message_topic, 'utf-8').encode() # 邮件主题 server = smtp.SMTP(smtp_server, 25) # SMTP协议默认端口是25 # 建立安全链接 server.starttls() server.set_debuglevel(1) server.login(from_addr, password) server.sendmail(from_addr, [to_addr], msg.as_string()) server.quit() if __name__ == '__main__': # 输入Email地址和口令: from_addr = '<EMAIL>' # from_addr = input('From: ') password = '<PASSWORD>' # password = input('Password: ') # 输入收件人地址: # to_addr = '<EMAIL>' to_addr = '<EMAIL>' # to_addr = input('To: ') # 输入SMTP服务器地址: smtp_server = 'smtp.qq.com' # smtp_server = input('SMTP server: ') # 输入邮件主题 message_topic = input('Message topic: ') message = input('Message: ') # send_pic = False send_file = False send2others(from_addr, password, to_addr, message_topic, message)
<gh_stars>1-10 """ Scan a storage containing raw geocatalogo datasets; extract candidate URLs for resources. """ from __future__ import print_function import cgi import sys import lxml.etree import requests from harvester.utils import get_plugin, XPathHelper from harvester_odt.pat_geocatalogo.converter import ( API_XML_NSMAP, LINKED_XML_NSMAP) def _check_url(url): try: response = requests.get(url, stream=True) except: return (None, []) else: status = response.status_code extras = [] content_type, ctextra = cgi.parse_header(response.headers['content-type']) if content_type == 'application/zip': extras.append(('zip', _check_zip(response))) return status, extras def _fmt_check_result(code): if code is None: return '\033[1;31mERR\033[0m' if 200 <= code < 300: return '\033[1;32m{0}\033[0m'.format(code) return '\033[1;32m{0}\033[0m'.format(code) def _fmt_check_extra(extra): res = [] for key, val in extra: val = '\033[1;32mSUCC\033[0m' if val else '\033[1;31mFAIL\033[0m' res.append('\033[0;36m{0}\033[0m:{1}'.format(key, val)) return " ".join(res) def _check_zip(response): from io import BytesIO import zipfile try: zf = zipfile.ZipFile(BytesIO(response.content)) except: return False filelist = zf.filelist if len(filelist) == 0: return False # Empty zip if sum(f.file_size for f in filelist) <= 0: return False # Zip containing empty files return True def main(storage): for dsid, dsobj in storage.documents['dataset'].iteritems(): print("\033[1;36mDATASET:\033[0m \033[36m{0}\033[0m".format(dsid)) dsxml1 = dsobj['raw_xml'].decode('latin-1') dsxml2 = storage.blobs['resource_xml'][dsid].decode('latin-1') dsxml1 = lxml.etree.fromstring(dsxml1) dsxml2 = lxml.etree.fromstring(dsxml2) xph1 = XPathHelper(dsxml1, nsmap=API_XML_NSMAP) xph2 = XPathHelper(dsxml2, nsmap=LINKED_XML_NSMAP) # ------------------------------------------------------------ # URLs from API XML _url_ogd_xml = xph1('geonet:info/ogd_xml/text()').get_one() _url_ogd_zip = xph1('geonet:info/ogd_zip/text()').get_one() _url_ogd_rdf = xph1('geonet:info/ogd_rdf/text()').get_one() urls = [ ('OGD XML url', _url_ogd_xml), ('OGD ZIP url', _url_ogd_zip), ('OGD RDF url', _url_ogd_rdf), ] for label, url in urls: if url is None: continue result, extra = _check_url(url) print(" {res} \033[33m{label}:\033[0m {url} {extra}" .format(url=url, label=label, extra=_fmt_check_extra(extra), res=_fmt_check_result(result))) # ------------------------------------------------------------ # Links from "Linked" XML links_xpath = ('/gmd:MD_Metadata/gmd:distributionInfo/' 'gmd:MD_Distribution/gmd:transferOptions/' 'gmd:MD_DigitalTransferOptions/gmd:onLine/' 'gmd:CI_OnlineResource/gmd:linkage/gmd:URL/text()') links = xph2(links_xpath) print("\n \033[1mLinks from ogd:xml:\033[0m") for link in links: result, extra = _check_url(link) if link in (_url_ogd_zip, _url_ogd_rdf): color = '\033[32m' else: color = '\033[31m' print(" {res} {color}{url}\033[0m {extra}" .format(color=color, url=link, extra=_fmt_check_extra(extra), res=_fmt_check_result(result))) print("") if __name__ == '__main__': storage = get_plugin('storage', sys.argv[1], []) main(storage)
<filename>TuneFindFromSeries/tunefind_crawler.py """ Download all songs of a TV show from youtube as mp3. Can also download individual songs. It all started with How I met your mother. As is well known, their music choice is excellent. So I wanted to download all the songs that appeared in HIMYM. So I wrote the following code. For actual download, it queues the url in IDM. You have to start those manually. Use cases are at the end of the code. We'll get to the code later, first an explanation. The site TuneFind lists music of Movies and TV shows, I extract the season list, episode list per season and songs list per episode from there. Now that I have the name of the song and the artist, I get the first search result on youtube with these keywords. Now that I have a youtube video id for a song, I send a request to YouTube to MP3 Converter to convert it to mp3 and get the url of the converted mp3. Download the mp3 in the correct heirarchical location. Finally, all the mp3s are downloaded and saved in respective folders for each episode for each season: """ import os import urllib.request, urllib.parse, urllib.error import re import pickle import html.parser import requests import time show_url = "http://www.tunefind.com/show/%s" season_url = "http://www.tunefind.com/show/%s/season-%d" episode_url = "http://www.tunefind.com/show/%s/season-%d/%s" def get_youtube_mp3_url(url): for i in range(2): statusurl = None r = requests.post("http://www.listentoyoutube.com/cc/conversioncloud.php", data={"mediaurl": url, "client_urlmap": "none"}) try: statusurl = eval(r.text)['statusurl'].replace('\\/', '/') + "&json" break except: print(eval(r.text)['error']) time.sleep(1) while True: if not statusurl: raise Exception("") try: resp = eval(requests.get(statusurl).text) if 'downloadurl' in resp: downloadurl = resp['downloadurl'].replace('\\/', '/') break time.sleep(1) except Exception: pass return downloadurl def urlopen(url, tries=10): exc = "Couldn't open url %s" % url for i in range(tries): try: #stream = urllib.request.urlopen(url) #Maneira antiga req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'} ) #Sem isso fica com 403 - Forbidden stream = urllib.request.urlopen(req) #.read() return str(stream.read()) except Exception as e: exc = e raise Exception(exc) def download_song(song, location): print(song) song_name = song[1] + " - " + song[0] + ".mp3" if not os.path.exists(location): os.makedirs(location) if not os.path.exists(os.path.join(location, song_name)): try: r = requests.get("YouTube", params={"search_query": "%s %s" % (song[1], song[0])}).text top_vid_id = re.findall(r'data-context-item-id="(.*?)"', r)[0] mp3_url = get_youtube_mp3_url("YouTube" + top_vid_id) #cmd = 'idman /d %s /p "%s" /f "%s" /a' % (mp3_url, os.path.join(os.getcwd(), location), song_name) #Mudar AQUI #os.system(cmd) #Rotina que baixa um arquivo (no caso, MP3) #file_name = mp3_url.split('/')[-1] file_name = song_name u = urllib2.urlopen(mp3_url) f = open(file_name, 'wb') meta = u.info() file_size = int(meta.getheaders("Content-Length")[0]) print( "Downloading: %s Bytes: %s" % (file_name, file_size) ) file_size_dl = 0 block_sz = 8192 while True: buffer = u.read(block_sz) if not buffer: break file_size_dl += len(buffer) f.write(buffer) status = r"%10d [%3.2f%%]" % (file_size_dl, file_size_dl * 100. / file_size) status = status + chr(8)*(len(status)+1) print (status), f.close() except Exception: raise def get_episode_music(show, season, episode): episode_num, episode_name = episode episode_dir = os.path.join(show, str(season), episode_name.replace(':', '')) episode_data_path = os.path.join(show, str(season), episode_name.replace(':', ''), "data") if not os.path.exists(episode_dir): os.mkdir(episode_dir) if os.path.exists(episode_data_path): with open(episode_data_path) as episode_data_file: episode_data = pickle.load(episode_data_file) else: episode_data = {} pg = urlopen(episode_url % (show, season, episode_num)) h = html.parser.HTMLParser() try: episode_data['songs'] = list((h.unescape(b), h.unescape(c), a) for (a, b, c, d) in re.findall(r'<a .*?name="song-\d+" href="(/song/\d+/\d+.*?)".*?><i.*?></i>(.*?)</a>\W*by (.*?)\W*<div.*?>\W*<div.*?>(.*?)</div>\W*</div>', pg)) except: episode_data['songs'] = list((b, c, a) for (a, b, c, d) in re.findall(r'<a .*?name="song-\d+" href="(/song/\d+/\d+.*?)".*?><i.*?></i>(.*?)</a>\W*by (.*?)\W*<div.*?>\W*<div.*?>(.*?)</div>\W*</div>', pg)) with open(episode_data_path, 'wb') as episode_data_file: pickle.dump(episode_data, episode_data_file) for song in episode_data['songs']: download_song(song, episode_dir) def get_season_music(show, season): season_dir = os.path.join(show, str(season)) season_data_path = os.path.join(show, str(season), 'data') if not os.path.exists(season_dir): os.mkdir(season_dir) if os.path.exists(season_data_path): with open(season_data_path, 'rb') as season_data_file: season_data = pickle.load(season_data_file) else: season_data = {} h = html.parser.HTMLParser() #print(season_url % (show, season)) pg = urlopen(season_url % (show, season)) #season_data['episodes'] = list((a, h.unescape(b)) for (a, b) in re.findall(r'<a href=".*?" name="episode(.*?)">\W*(.*?)\W*</a>', pg)) season_data['episodes'] = list((a, h.unescape(b)) for (a, b) in re.findall(r'<a href=".*?" data-reactid=".*?">\W*(.*?)\W*</a>', pg)) with open(season_data_path, 'wb') as season_data_file: pickle.dump(season_data, season_data_file) print( season_data ) x = 1/0 print("Temporada#", season, ", total de episodios=", len(season_data['episodes']) ) for episode in season_data['episodes']: get_episode_music(show, season, episode) def get_show_music(show): if not os.path.exists(show): os.mkdir(show) if os.path.exists(os.path.join(show, 'data')): with open(os.path.join(show, 'data'), 'rb') as show_data_file: show_data = pickle.load(show_data_file) else: show_data = {} slug = show.lower().replace(' ', '-') #pg = urlopen(show_url % show).read().decode('utf-8') pg = urlopen(show_url % show) #.decode('utf-8') season_finder_pattern = r'/show/' + slug + r'/season-\d+' print(season_finder_pattern) season_links = list(set(re.findall(season_finder_pattern, pg))) season_links.sort() seasons = list(int(sl[sl.find('season-') + len('season-'):]) for sl in season_links) seasons.sort() show_data['seasons'] = seasons with open(os.path.join(show, 'data'), 'wb') as show_data_file: pickle.dump(show_data, show_data_file) # print show_data print("Seriado=",show,", Temporadas=", len(show_data['seasons']) ) for season in show_data['seasons']: get_season_music(show, season) os.system("idman /s") if __name__ == '__main__': # download_song(["Humme hai hero", "A R Rahman"], "Misc") #download_song(["Caravan (Instrumental) [Remastered]","<NAME>"], "Misc") #get_show_music("How I Met Your Mother") get_show_music("Mad Men")
#!/usr/bin/python # # Copyright 2014 Huawei Technologies Co. Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """test hdsdiscovery base module.""" import os import unittest2 from mock import patch os.environ['COMPASS_IGNORE_SETTING'] = 'true' from compass.utils import setting_wrapper as setting reload(setting) from compass.hdsdiscovery.base import BaseSnmpMacPlugin from compass.hdsdiscovery.base import BaseSnmpVendor from compass.hdsdiscovery.error import TimeoutError from compass.utils import flags from compass.utils import logsetting class MockSnmpVendor(BaseSnmpVendor): """snmp vendor mock class.""" def __init__(self): BaseSnmpVendor.__init__(self, ["MockVendor", "FakeVendor"]) class TestBaseSnmpMacPlugin(unittest2.TestCase): """teset base snmp plugin class.""" def setUp(self): super(TestBaseSnmpMacPlugin, self).setUp() logsetting.init() self.test_plugin = BaseSnmpMacPlugin('127.0.0.1', {'version': '2c', 'community': 'public'}) def tearDown(self): del self.test_plugin super(TestBaseSnmpMacPlugin, self).tearDown() @patch('compass.hdsdiscovery.utils.snmpget_by_cl') def test_get_port(self, mock_snmpget): """test snmp get port.""" # Successfully get port number mock_snmpget.return_value = 'IF-MIB::ifName.4 = STRING: ge-1/1/4' result = self.test_plugin.get_port('4') self.assertEqual('4', result) # Failed to get port number, switch is timeout mock_snmpget.side_effect = TimeoutError("Timeout") result = self.test_plugin.get_port('4') self.assertIsNone(result) @patch('compass.hdsdiscovery.utils.snmpget_by_cl') def test_get_vlan_id(self, mock_snmpget): """test snmp get vlan.""" # Port is None self.assertIsNone(self.test_plugin.get_vlan_id(None)) # Port is not None mock_snmpget.return_value = 'Q-BRIDGE-MIB::dot1qPvid.4 = Gauge32: 100' result = self.test_plugin.get_vlan_id('4') self.assertEqual('100', result) # Faild to query switch due to timeout mock_snmpget.side_effect = TimeoutError("Timeout") result = self.test_plugin.get_vlan_id('4') self.assertIsNone(result) def test_get_mac_address(self): """tet snmp get mac address.""" # Correct input for mac numbers mac_numbers = '0.224.129.230.57.173'.split('.') mac = self.test_plugin.get_mac_address(mac_numbers) self.assertEqual('00:e0:81:e6:39:ad', mac) # Incorrct input for mac numbers mac_numbers = '0.224.129.230.57'.split('.') mac = self.test_plugin.get_mac_address(mac_numbers) self.assertIsNone(mac) class BaseTest(unittest2.TestCase): """base test class.""" def setUp(self): super(BaseTest, self).setUp() logsetting.init() def tearDown(self): super(BaseTest, self).tearDown() def test_base_snmp_vendor(self): """test base snmp vendor.""" fake = MockSnmpVendor() is_vendor = fake.is_this_vendor("FakeVendor 1.1") self.assertTrue(is_vendor) # check case-insensitive match self.assertFalse(fake.is_this_vendor("fakevendor1.1")) # breaks word-boudary match self.assertFalse(fake.is_this_vendor("FakeVendor1.1")) if __name__ == '__main__': flags.init() logsetting.init() unittest2.main()
<reponame>MTD-group/amlt import numpy as np from . import struct_colors, dyn_markers, dyn_types, struct_types from . import read_evaluation_data, get_force_list, compute_force_error_list from . import compute_rms_force_error_by_atom, compute_rms_force_error_by_image from . import compute_force_norms_by_image, collapse_sub_lists from . import compute_force_cosines_by_atom, compute_force_norms_by_atom from . import nice_bins_percentile from matplotlib import cm formula_angle = r"$\theta_\mathbf{F} = \cos^{-1} \left ( \frac{\mathbf{F}_{MLIP} \cdot \mathbf{F}_{DFT} }{\left | \mathbf{F}_{MLIP} \right | \left | \mathbf{F}_{DFT} \right |} \right )$" def plot_force_angle_heatmaps(axes, data_sets, struct_types = struct_types, dyn_types = dyn_types, struct_colors = struct_colors, dyn_markers = dyn_markers, bad_data_traj_list = [], cmap = cm.get_cmap('plasma')): deg = 180/np.pi bin_size = 2 my_bins = np.arange(0,180+bin_size/2, bin_size) ylims = (0,180) from matplotlib.ticker import MultipleLocator import copy cmap_tweaked = copy.copy(cmap) from matplotlib.colors import Colormap Colormap.set_under(cmap_tweaked, color=(1,1,1,0)) Colormap.set_over(cmap_tweaked, color=(1,1,1,0)) for di, data_set in enumerate(data_sets): fname = data_set[0] data_name = data_set[1] #color = np.array(get_color(data_set[2])) #lightness = data_set[3] #zorder = zorders[di] image_pairs = read_evaluation_data(filename = fname, struct_types = struct_types, dyn_types = dyn_types) cache_forces, data_forces = get_force_list(image_pairs) force_cosines_by_atom = compute_force_cosines_by_atom(cache_forces, data_forces) data_force_norms_by_atom = compute_force_norms_by_atom(data_forces) X = collapse_sub_lists(data_force_norms_by_atom ) Y = deg*np.arccos( collapse_sub_lists(force_cosines_by_atom)) xbins = np.linspace(0,X.max(), 100) axes[di].hist2d(X, Y, bins = (xbins, my_bins), vmin=1, cmap = cmap_tweaked) #label = data_name + '\nMean: %.2f°\nRMS: %.2f°'%(mean_force_angles, rms_force_angles) axes[di].set_title(data_name, fontsize= 8) axes[di].set_ylim(ylims ) #ax2.legend(fontsize = 8, handletextpad = 0.3, borderpad = 0.1) axes[di].minorticks_on() axes[di].yaxis.set_major_locator(MultipleLocator(base=30)) axes[di].yaxis.set_minor_locator(MultipleLocator(base=5)) if di == 0: axes[di].set_ylabel('Force Angle, '+formula_angle +' (°)') axes[di].set_xlabel('DFT Force (eV/Å)') #axes[di].set_ylabel('MLIP Force Error (eV/Å)')
"""The tests for the analytics .""" from unittest.mock import AsyncMock, Mock, patch import aiohttp import pytest from homeassistant.components.analytics.analytics import Analytics from homeassistant.components.analytics.const import ( ANALYTICS_ENDPOINT_URL, ATTR_BASE, ATTR_DIAGNOSTICS, ATTR_PREFERENCES, ATTR_STATISTICS, ATTR_USAGE, ) from homeassistant.const import __version__ as HA_VERSION from homeassistant.loader import IntegrationNotFound MOCK_HUUID = "abcdefg" async def test_no_send(hass, caplog, aioclient_mock): """Test send when no prefrences are defined.""" aioclient_mock.post(ANALYTICS_ENDPOINT_URL, status=200) analytics = Analytics(hass) with patch( "homeassistant.components.hassio.is_hassio", side_effect=Mock(return_value=False), ), patch("homeassistant.helpers.instance_id.async_get", return_value=MOCK_HUUID): await analytics.load() assert not analytics.preferences[ATTR_BASE] await analytics.send_analytics() assert "Nothing to submit" in caplog.text assert len(aioclient_mock.mock_calls) == 0 async def test_load_with_supervisor_diagnostics(hass): """Test loading with a supervisor that has diagnostics enabled.""" analytics = Analytics(hass) assert not analytics.preferences[ATTR_DIAGNOSTICS] with patch( "homeassistant.components.hassio.get_supervisor_info", side_effect=Mock(return_value={"diagnostics": True}), ), patch( "homeassistant.components.hassio.is_hassio", side_effect=Mock(return_value=True), ): await analytics.load() assert analytics.preferences[ATTR_DIAGNOSTICS] async def test_load_with_supervisor_without_diagnostics(hass): """Test loading with a supervisor that has not diagnostics enabled.""" analytics = Analytics(hass) analytics._data[ATTR_PREFERENCES][ATTR_DIAGNOSTICS] = True assert analytics.preferences[ATTR_DIAGNOSTICS] with patch( "homeassistant.components.hassio.get_supervisor_info", side_effect=Mock(return_value={"diagnostics": False}), ), patch( "homeassistant.components.hassio.is_hassio", side_effect=Mock(return_value=True), ): await analytics.load() assert not analytics.preferences[ATTR_DIAGNOSTICS] async def test_failed_to_send(hass, caplog, aioclient_mock): """Test failed to send payload.""" aioclient_mock.post(ANALYTICS_ENDPOINT_URL, status=400) analytics = Analytics(hass) await analytics.save_preferences({ATTR_BASE: True}) assert analytics.preferences[ATTR_BASE] with patch("homeassistant.helpers.instance_id.async_get", return_value=MOCK_HUUID): await analytics.send_analytics() assert "Sending analytics failed with statuscode 400" in caplog.text async def test_failed_to_send_raises(hass, caplog, aioclient_mock): """Test raises when failed to send payload.""" aioclient_mock.post(ANALYTICS_ENDPOINT_URL, exc=aiohttp.ClientError()) analytics = Analytics(hass) await analytics.save_preferences({ATTR_BASE: True}) assert analytics.preferences[ATTR_BASE] with patch("homeassistant.helpers.instance_id.async_get", return_value=MOCK_HUUID): await analytics.send_analytics() assert "Error sending analytics" in caplog.text async def test_send_base(hass, caplog, aioclient_mock): """Test send base prefrences are defined.""" aioclient_mock.post(ANALYTICS_ENDPOINT_URL, status=200) analytics = Analytics(hass) await analytics.save_preferences({ATTR_BASE: True}) assert analytics.preferences[ATTR_BASE] with patch("homeassistant.helpers.instance_id.async_get", return_value=MOCK_HUUID): await analytics.send_analytics() assert f"'huuid': '{MOCK_HUUID}'" in caplog.text assert f"'version': '{HA_VERSION}'" in caplog.text assert "'installation_type':" in caplog.text assert "'integration_count':" not in caplog.text assert "'integrations':" not in caplog.text async def test_send_base_with_supervisor(hass, caplog, aioclient_mock): """Test send base prefrences are defined.""" aioclient_mock.post(ANALYTICS_ENDPOINT_URL, status=200) analytics = Analytics(hass) await analytics.save_preferences({ATTR_BASE: True}) assert analytics.preferences[ATTR_BASE] with patch( "homeassistant.components.hassio.get_supervisor_info", side_effect=Mock(return_value={"supported": True, "healthy": True}), ), patch( "homeassistant.components.hassio.get_info", side_effect=Mock(return_value={}), ), patch( "homeassistant.components.hassio.get_host_info", side_effect=Mock(return_value={}), ), patch( "homeassistant.components.hassio.is_hassio", side_effect=Mock(return_value=True), ), patch( "homeassistant.helpers.instance_id.async_get", return_value=MOCK_HUUID ): await analytics.send_analytics() assert f"'huuid': '{MOCK_HUUID}'" in caplog.text assert f"'version': '{HA_VERSION}'" in caplog.text assert "'supervisor': {'healthy': True, 'supported': True}}" in caplog.text assert "'installation_type':" in caplog.text assert "'integration_count':" not in caplog.text assert "'integrations':" not in caplog.text async def test_send_usage(hass, caplog, aioclient_mock): """Test send usage prefrences are defined.""" aioclient_mock.post(ANALYTICS_ENDPOINT_URL, status=200) analytics = Analytics(hass) await analytics.save_preferences({ATTR_BASE: True, ATTR_USAGE: True}) assert analytics.preferences[ATTR_BASE] assert analytics.preferences[ATTR_USAGE] hass.config.components = ["default_config"] with patch("homeassistant.helpers.instance_id.async_get", return_value=MOCK_HUUID): await analytics.send_analytics() assert "'integrations': ['default_config']" in caplog.text assert "'integration_count':" not in caplog.text async def test_send_usage_with_supervisor(hass, caplog, aioclient_mock): """Test send usage with supervisor prefrences are defined.""" aioclient_mock.post(ANALYTICS_ENDPOINT_URL, status=200) analytics = Analytics(hass) await analytics.save_preferences({ATTR_BASE: True, ATTR_USAGE: True}) assert analytics.preferences[ATTR_BASE] assert analytics.preferences[ATTR_USAGE] hass.config.components = ["default_config"] with patch( "homeassistant.components.hassio.get_supervisor_info", side_effect=Mock( return_value={ "healthy": True, "supported": True, "addons": [{"slug": "test_addon"}], } ), ), patch( "homeassistant.components.hassio.get_info", side_effect=Mock(return_value={}), ), patch( "homeassistant.components.hassio.get_host_info", side_effect=Mock(return_value={}), ), patch( "homeassistant.components.hassio.async_get_addon_info", side_effect=AsyncMock( return_value={ "slug": "test_addon", "protected": True, "version": "1", "auto_update": False, } ), ), patch( "homeassistant.components.hassio.is_hassio", side_effect=Mock(return_value=True), ), patch( "homeassistant.helpers.instance_id.async_get", return_value=MOCK_HUUID ): await analytics.send_analytics() assert ( "'addons': [{'slug': 'test_addon', 'protected': True, 'version': '1', 'auto_update': False}]" in caplog.text ) assert "'addon_count':" not in caplog.text async def test_send_statistics(hass, caplog, aioclient_mock): """Test send statistics prefrences are defined.""" aioclient_mock.post(ANALYTICS_ENDPOINT_URL, status=200) analytics = Analytics(hass) await analytics.save_preferences({ATTR_BASE: True, ATTR_STATISTICS: True}) assert analytics.preferences[ATTR_BASE] assert analytics.preferences[ATTR_STATISTICS] hass.config.components = ["default_config"] with patch("homeassistant.helpers.instance_id.async_get", return_value=MOCK_HUUID): await analytics.send_analytics() assert ( "'state_count': 0, 'automation_count': 0, 'integration_count': 1, 'user_count': 0" in caplog.text ) assert "'integrations':" not in caplog.text async def test_send_statistics_one_integration_fails(hass, caplog, aioclient_mock): """Test send statistics prefrences are defined.""" aioclient_mock.post(ANALYTICS_ENDPOINT_URL, status=200) analytics = Analytics(hass) await analytics.save_preferences({ATTR_BASE: True, ATTR_STATISTICS: True}) assert analytics.preferences[ATTR_BASE] assert analytics.preferences[ATTR_STATISTICS] hass.config.components = ["default_config"] with patch( "homeassistant.components.analytics.analytics.async_get_integration", side_effect=IntegrationNotFound("any"), ), patch("homeassistant.helpers.instance_id.async_get", return_value=MOCK_HUUID): await analytics.send_analytics() post_call = aioclient_mock.mock_calls[0] assert "huuid" in post_call[2] assert post_call[2]["integration_count"] == 0 async def test_send_statistics_async_get_integration_unknown_exception( hass, caplog, aioclient_mock ): """Test send statistics prefrences are defined.""" aioclient_mock.post(ANALYTICS_ENDPOINT_URL, status=200) analytics = Analytics(hass) await analytics.save_preferences({ATTR_BASE: True, ATTR_STATISTICS: True}) assert analytics.preferences[ATTR_BASE] assert analytics.preferences[ATTR_STATISTICS] hass.config.components = ["default_config"] with pytest.raises(ValueError), patch( "homeassistant.components.analytics.analytics.async_get_integration", side_effect=ValueError, ), patch("homeassistant.helpers.instance_id.async_get", return_value=MOCK_HUUID): await analytics.send_analytics() async def test_send_statistics_with_supervisor(hass, caplog, aioclient_mock): """Test send statistics prefrences are defined.""" aioclient_mock.post(ANALYTICS_ENDPOINT_URL, status=200) analytics = Analytics(hass) await analytics.save_preferences({ATTR_BASE: True, ATTR_STATISTICS: True}) assert analytics.preferences[ATTR_BASE] assert analytics.preferences[ATTR_STATISTICS] with patch( "homeassistant.components.hassio.get_supervisor_info", side_effect=Mock( return_value={ "healthy": True, "supported": True, "addons": [{"slug": "test_addon"}], } ), ), patch( "homeassistant.components.hassio.get_info", side_effect=Mock(return_value={}), ), patch( "homeassistant.components.hassio.get_host_info", side_effect=Mock(return_value={}), ), patch( "homeassistant.components.hassio.async_get_addon_info", side_effect=AsyncMock( return_value={ "slug": "test_addon", "protected": True, "version": "1", "auto_update": False, } ), ), patch( "homeassistant.components.hassio.is_hassio", side_effect=Mock(return_value=True), ), patch( "homeassistant.helpers.instance_id.async_get", return_value=MOCK_HUUID ): await analytics.send_analytics() assert "'addon_count': 1" in caplog.text assert "'integrations':" not in caplog.text
from manul_utils import SHM_SIZE from typing import Tuple import logging import numpy as np import random import string from fuzzwatch_state import BITMAP_SIZE, ROW_SIZE LOG_FILE = 'gui_log.txt' def get_logger() -> logging.Logger: logger = logging.getLogger('__FILE__') logger.setLevel(logging.DEBUG) # create file handler which logs even debug messages fh = logging.FileHandler(LOG_FILE) fh.setLevel(logging.DEBUG) formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') fh.setFormatter(formatter) logger.addHandler(fh) return logger def get_rand_bytes(n: int) -> bytes: """Get a random string of bytes of length n""" return bytes([random.getrandbits(8) for _ in range(n)]) def format_hexdump( dump_data: bytes, data_offset: int=0, ): """Return an xxd-style hexdump with NULL byte separator between columns""" printable_chars = string.ascii_letters + string.digits + string.punctuation + ' ' dump_size = len(dump_data) num_hex_digits = len(f'{len(dump_data)+data_offset:x}') # Any respectable hexdump has at least four digits of offset if num_hex_digits < 4: num_hex_digits = 4 output = '' for i in range(0, dump_size, ROW_SIZE): row_data = dump_data[i:i+ROW_SIZE] row_offset = '{offset:0{width}x}'.format(offset=data_offset+i, width=num_hex_digits) row_hex = '' row_ascii = '' for j in range(ROW_SIZE): if j >= len(row_data): row_hex += ' ' else: row_hex += f' {row_data[j]:02x}' cur_char = chr(row_data[j]) if cur_char in printable_chars: row_ascii += cur_char else: row_ascii += '.' # break up long rows if (j+1) % 8 == 0: row_hex += ' ' # Using a non-printable separator for ease of re-parsing by highlighting code output += f'{row_offset}:\x00{row_hex}\x00{row_ascii}\n' return output.strip() def calculate_row_indices( row_start: int, mod_start: int, mod_end: int ) -> Tuple[int, int]: """Helper for determining the part of the row that needs highlighting""" if row_start > mod_start: highlight_start = 0 else: highlight_start = mod_start - row_start highlight_stop = (mod_end+1) - row_start if highlight_stop > ROW_SIZE: highlight_stop = ROW_SIZE return highlight_start, highlight_stop def split_hex_row( row_hex: str, start_index: int, stop_index: int ) -> Tuple[str, str, str]: """Helper for figuring out how to cleanly split a hex row for highlights""" # bytes are 2 hex chars plus 1 space, plus extra space every 8 bytes start_index = start_index * 3 + int(start_index / 8) stop_index = stop_index * 3 + int(stop_index / 8) before_hex = row_hex[:start_index] during_hex = row_hex[start_index:stop_index] after_hex = row_hex[stop_index:] return before_hex, during_hex, after_hex def summarize_bitmap(bitmap: list) -> str: """Give a compact text summary for sparse bitmaps""" nonzero_bits = [] for i, b in enumerate(bitmap): if b != 0: nonzero_bits.append(f'{i:x}:{b:02x}') sorted_nonzero_bits = ', '.join(sorted(nonzero_bits)) summary = f'{len(nonzero_bits)}/{len(bitmap)}: {sorted_nonzero_bits}' return summary hamming_weights = bytes(bin(x).count("1") for x in range(256)) def get_bitmap_coverage_stats(bitmap: list) -> Tuple[int, int]: """Get bits/bytes covered""" bits_set = 0 nonzero_bytes = 0 for i, b in enumerate(bitmap): if b != 0: nonzero_bytes += 1 bits_set += hamming_weights[b] return nonzero_bytes, bits_set fake_bitmap = None def use_fake_bitmap(ignored_bitmap: bytes) -> np.ndarray: """Return a bitmap getting more saturated, for demonstration purposes""" global fake_bitmap if fake_bitmap is None: fake_bitmap = bytearray(BITMAP_SIZE+1) add_factor = random.randint(8, 1024) adds = 0 prev_index = -1 repeat_percent = 25 while adds < add_factor: if prev_index == -1 or random.randint(0, 99) < repeat_percent: rand_index = random.randint(0, BITMAP_SIZE) else: rand_index = prev_index if fake_bitmap[rand_index] != 255: fake_bitmap[rand_index] += 1 adds += 1 prev_index = rand_index return bytes_to_matrix(fake_bitmap) def bytes_to_matrix(byte_str: bytes, do_mix: bool=False) -> np.ndarray: """Format the bitmap so it can been ingested by matplotlib's matshow""" if len(byte_str) < BITMAP_SIZE: raise Exception(f'bytes_to_matrix(): incorrect input length: {len(byte_str)}') if do_mix: byte_str = mix_64k(byte_str) # tack on an extra byte if given 65,535 so we can make it square if len(byte_str) == BITMAP_SIZE: byte_str += b'\x00' data_array = np.frombuffer(byte_str, dtype='ubyte') return data_array.reshape(256, 256) shuffled_array = None def mix_64k(byte_str: bytes) -> bytes: global shuffled_array if shuffled_array is None: shuffled_array = list(range(BITMAP_SIZE)) # Deterministically shuffle the array so it's the same across runs shuffler = random.Random() shuffler.seed(0) shuffler.shuffle(shuffled_array) if len(byte_str) != len(shuffled_array): raise Exception(f'mix_array got arg of len {len(byte_str)}, expected: {len(shuffled_array)}') # Use shuffled array to map indexes to a new deterministically mixed index return bytes( byte_str[shuffled_array[i]] for i in range(SHM_SIZE) )
<reponame>tarun-bisht/security-camera-tflite import os import time import cv2 import secrets import tensorflow as tf import numpy as np from absl import app, flags, logging from absl.flags import FLAGS from src.parse_args import get_security_cam_arguments from src.utils import ( VideoStream, draw_boxes, create_mail_msg, recover_videos, get_category_index, SaveVideo, SendMail, get_image_bytes, preprocess_input, ) flags.DEFINE_string( "config", "configs/security_cam.cfg", "path to config file for application" ) flags.DEFINE_bool("live", True, "Show live feed or not") # Read email and password to send mail to recepients from system environment so create these fields as system environment variables EMAIL_ADDRESS = os.getenv("EMAIL_ADDRESS") EMAIL_PASSWORD = os.getenv("EMAIL_PASSWORD") def main(_argv): # helper variables record = False num_categories = 4 next_active = [0] * num_categories record_active = 0 freq = cv2.getTickFrequency() tmp_folder = None frame_count = 0 # parse config file arguments args, security_cam_category_index = get_security_cam_arguments(FLAGS) min_threshold = args.get("min_threshold") fps = args.get("fps") wait_time = args.get("wait_after_message_send") min_record_time = args.get("min_recording_time") temp_path = args.get("temp_dir") record_path = args.get("recording_save_path") recipients = args.get("send_mail_to") neglect_categories = args.get("neglect_categories") interpreter = tf.lite.Interpreter(FLAGS.model) input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() interpreter.resize_tensor_input(input_details[0]["index"], [1, 320, 320, 3]) interpreter.allocate_tensors() logging.info("...model loaded...") # collect index of categories to neglect (do not detect) from all categories in labels model trained neglect = [] if neglect_categories: for key, value in security_cam_category_index.items(): if value["name"] in neglect_categories: neglect.append(value["id"]) # create a temp directory to save frames when intruder detected to create video. os.makedirs(temp_path, exist_ok=True) # initiating stream capture cap = VideoStream(resolution=args.get("res"), cam=args.get("cam")).start() width = int(cap.stream.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.stream.get(cv2.CAP_PROP_FRAME_HEIGHT)) # recover previously failed videos whose frames are currently stored in temp folder. Video generation failed at that time because of some system failure recover_videos(temp_path, record_path, fps, resolution=(width, height)) # initiating mailer mailer = SendMail( mail_id=EMAIL_ADDRESS, password=<PASSWORD>, recipients=recipients ) # detect intruder in stream while True: t1 = cv2.getTickCount() img = cap.read() image_tensor = np.expand_dims(img, axis=0) image_tensor = preprocess_input(image_tensor) interpreter.set_tensor(input_details[0]["index"], image_tensor) interpreter.invoke() boxes = interpreter.get_tensor(output_details[3]["index"])[0] classes = interpreter.get_tensor(output_details[4]["index"])[0] scores = interpreter.get_tensor(output_details[0]["index"])[0] # draw bounding box in frame frame = img.copy() frame = draw_boxes( frame, boxes, classes, scores, security_cam_category_index, height, width, min_threshold=min_threshold, put_label=False, ) # when a new category detected start recording video and send mail to notify and record till category is being detected. # sends a mail for every new category. category = int(get_category_index(scores, classes, min_threshold=min_threshold)) if category not in neglect and 0 < category <= num_categories: if time.time() > next_active[category - 1]: next_active[category - 1] = time.time() + wait_time class_name = security_cam_category_index[category]["name"] mailer.send_mail(create_mail_msg(class_name), get_image_bytes(frame)) if tmp_folder is None: tmp_folder = os.path.join(temp_path, f"{secrets.token_hex(8)}") os.makedirs(tmp_folder, exist_ok=True) frame_count = 0 record = True record_active = time.time() + min_record_time # print local time to frame local_time = time.strftime("%a %d-%b-%Y %H:%M:%S", time.localtime()) cv2.putText( frame, f"{local_time}", (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 255), 1, cv2.LINE_AA, ) # calculate fps and print it to frame t2 = cv2.getTickCount() t = (t2 - t1) / freq cv2.putText( frame, f"FPS: {1 / t:.2f}".format(), (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 0), 1, cv2.LINE_AA, ) # saving recording frames if record and tmp_folder is not None: cv2.imwrite( os.path.join( tmp_folder, f"{frame_count:08}_{secrets.token_hex(4)}.jpg" ), frame, ) if time.time() > record_active: logging.info("...recording stopped, generating video...") record = False # create video from frames in a new thread SaveVideo(tmp_folder, record_path, fps, (width, height)).save() tmp_folder = None frame_count += 1 # if live enable then show UI if FLAGS.live: cv2.imshow("Intelligent Security Camera", cv2.resize(frame, (800, 600))) if cv2.waitKey(1) & 0xFF == ord("q"): break cap.stop() if __name__ == "__main__": try: app.run(main) except SystemExit: logging.error("Exiting")
<filename>pinax/apps/blog/views.py<gh_stars>0 import datetime from django.conf import settings from django.core.urlresolvers import reverse from django.http import HttpResponseRedirect, Http404 from django.shortcuts import render_to_response, get_object_or_404 from django.template import RequestContext from django.utils.translation import ugettext, ugettext_lazy as _ from django.views.generic import date_based from django.contrib import messages from django.contrib.auth.models import User from django.contrib.auth.decorators import login_required from pinax.apps.blog.models import Post from pinax.apps.blog.forms import * if "notification" in settings.INSTALLED_APPS: from notification import models as notification else: notification = None try: from friends.models import Friendship friends = True except ImportError: friends = False def blogs(request, username=None, template_name="blog/blogs.html"): blogs = Post.objects.filter(status=2).select_related(depth=1).order_by("-publish") if username is not None: user = get_object_or_404(User, username=username.lower()) blogs = blogs.filter(author=user) return render_to_response(template_name, { "blogs": blogs, }, context_instance=RequestContext(request)) def post(request, username, year, month, slug, template_name="blog/post.html"): post = Post.objects.filter( slug = slug, publish__year = int(year), publish__month = int(month), author__username = username ) if not post: raise Http404 if post[0].status == 1 and post[0].author != request.user: raise Http404 return render_to_response(template_name, { "post": post[0], }, context_instance=RequestContext(request)) @login_required def your_posts(request, template_name="blog/your_posts.html"): return render_to_response(template_name, { "blogs": Post.objects.filter(author=request.user), }, context_instance=RequestContext(request)) @login_required def destroy(request, id): post = Post.objects.get(pk=id) user = request.user title = post.title if post.author != request.user: messages.add_message(request, messages.ERROR, ugettext("You can't delete posts that aren't yours") ) return HttpResponseRedirect(reverse("blog_list_yours")) if request.method == "POST" and request.POST["action"] == "delete": post.delete() messages.add_message(request, messages.SUCCESS, ugettext("Successfully deleted post '%s'") % title ) return HttpResponseRedirect(reverse("blog_list_yours")) else: return HttpResponseRedirect(reverse("blog_list_yours")) return render_to_response(context_instance=RequestContext(request)) @login_required def new(request, form_class=BlogForm, template_name="blog/new.html"): if request.method == "POST": if request.POST["action"] == "create": blog_form = form_class(request.user, request.POST) if blog_form.is_valid(): blog = blog_form.save(commit=False) blog.author = request.user if getattr(settings, 'BEHIND_PROXY', False): blog.creator_ip = request.META["HTTP_X_FORWARDED_FOR"] else: blog.creator_ip = request.META['REMOTE_ADDR'] blog.save() # @@@ should message be different if published? messages.add_message(request, messages.SUCCESS, ugettext("Successfully saved post '%s'") % blog.title ) if notification: if blog.status == 2: # published if friends: # @@@ might be worth having a shortcut for sending to all friends notification.send((x['friend'] for x in Friendship.objects.friends_for_user(blog.author)), "blog_friend_post", {"post": blog}) return HttpResponseRedirect(reverse("blog_list_yours")) else: blog_form = form_class() else: blog_form = form_class() return render_to_response(template_name, { "blog_form": blog_form }, context_instance=RequestContext(request)) @login_required def edit(request, id, form_class=BlogForm, template_name="blog/edit.html"): post = get_object_or_404(Post, id=id) if request.method == "POST": if post.author != request.user: messages.add_message(request, messages.ERROR, ugettext("You can't edit posts that aren't yours") ) return HttpResponseRedirect(reverse("blog_list_yours")) if request.POST["action"] == "update": blog_form = form_class(request.user, request.POST, instance=post) if blog_form.is_valid(): blog = blog_form.save(commit=False) blog.save() messages.add_message(request, messages.SUCCESS, ugettext("Successfully updated post '%s'") % blog.title ) if notification: if blog.status == 2: # published if friends: # @@@ might be worth having a shortcut for sending to all friends notification.send((x['friend'] for x in Friendship.objects.friends_for_user(blog.author)), "blog_friend_post", {"post": blog}) return HttpResponseRedirect(reverse("blog_list_yours")) else: blog_form = form_class(instance=post) else: blog_form = form_class(instance=post) return render_to_response(template_name, { "blog_form": blog_form, "post": post, }, context_instance=RequestContext(request))
# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: face_detection.proto from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() DESCRIPTOR = _descriptor.FileDescriptor( name='face_detection.proto', package='FaceDetection', syntax='proto3', serialized_options=b'\n%com.bytedance.videoarch.facedetectionB\tFaceProtoP\001\242\002\003RTG', create_key=_descriptor._internal_create_key, serialized_pb=b'\n\x14\x66\x61\x63\x65_detection.proto\x12\rFaceDetection\"L\n\x0b\x44\x65tectedObj\x12\n\n\x02lx\x18\x02 \x01(\x05\x12\n\n\x02ly\x18\x03 \x01(\x05\x12\n\n\x02rx\x18\x04 \x01(\x05\x12\n\n\x02ry\x18\x05 \x01(\x05\x12\r\n\x05score\x18\x07 \x01(\x02\"E\n\x0e\x46\x61\x63\x65\x44\x65tRequest\x12\x11\n\timageData\x18\x01 \x01(\x0c\x12\x0c\n\x04type\x18\x02 \x01(\t\x12\x12\n\nconfThresh\x18\x03 \x01(\x02\">\n\x0f\x46\x61\x63\x65\x44\x65tResponse\x12+\n\x07\x64\x65tObjs\x18\x01 \x03(\x0b\x32\x1a.FaceDetection.DetectedObj2\\\n\x0e\x46\x61\x63\x65\x44\x65tService\x12J\n\x07predict\x12\x1d.FaceDetection.FaceDetRequest\x1a\x1e.FaceDetection.FaceDetResponse\"\x00\x42:\n%com.bytedance.videoarch.facedetectionB\tFaceProtoP\x01\xa2\x02\x03RTGb\x06proto3' ) _DETECTEDOBJ = _descriptor.Descriptor( name='DetectedObj', full_name='FaceDetection.DetectedObj', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='lx', full_name='FaceDetection.DetectedObj.lx', index=0, number=2, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='ly', full_name='FaceDetection.DetectedObj.ly', index=1, number=3, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='rx', full_name='FaceDetection.DetectedObj.rx', index=2, number=4, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='ry', full_name='FaceDetection.DetectedObj.ry', index=3, number=5, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='score', full_name='FaceDetection.DetectedObj.score', index=4, number=7, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=39, serialized_end=115, ) _FACEDETREQUEST = _descriptor.Descriptor( name='FaceDetRequest', full_name='FaceDetection.FaceDetRequest', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='imageData', full_name='FaceDetection.FaceDetRequest.imageData', index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b"", message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='type', full_name='FaceDetection.FaceDetRequest.type', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='confThresh', full_name='FaceDetection.FaceDetRequest.confThresh', index=2, number=3, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=117, serialized_end=186, ) _FACEDETRESPONSE = _descriptor.Descriptor( name='FaceDetResponse', full_name='FaceDetection.FaceDetResponse', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='detObjs', full_name='FaceDetection.FaceDetResponse.detObjs', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=188, serialized_end=250, ) _FACEDETRESPONSE.fields_by_name['detObjs'].message_type = _DETECTEDOBJ DESCRIPTOR.message_types_by_name['DetectedObj'] = _DETECTEDOBJ DESCRIPTOR.message_types_by_name['FaceDetRequest'] = _FACEDETREQUEST DESCRIPTOR.message_types_by_name['FaceDetResponse'] = _FACEDETRESPONSE _sym_db.RegisterFileDescriptor(DESCRIPTOR) DetectedObj = _reflection.GeneratedProtocolMessageType('DetectedObj', (_message.Message,), { 'DESCRIPTOR' : _DETECTEDOBJ, '__module__' : 'face_detection_pb2' # @@protoc_insertion_point(class_scope:FaceDetection.DetectedObj) }) _sym_db.RegisterMessage(DetectedObj) FaceDetRequest = _reflection.GeneratedProtocolMessageType('FaceDetRequest', (_message.Message,), { 'DESCRIPTOR' : _FACEDETREQUEST, '__module__' : 'face_detection_pb2' # @@protoc_insertion_point(class_scope:FaceDetection.FaceDetRequest) }) _sym_db.RegisterMessage(FaceDetRequest) FaceDetResponse = _reflection.GeneratedProtocolMessageType('FaceDetResponse', (_message.Message,), { 'DESCRIPTOR' : _FACEDETRESPONSE, '__module__' : 'face_detection_pb2' # @@protoc_insertion_point(class_scope:FaceDetection.FaceDetResponse) }) _sym_db.RegisterMessage(FaceDetResponse) DESCRIPTOR._options = None _FACEDETSERVICE = _descriptor.ServiceDescriptor( name='FaceDetService', full_name='FaceDetection.FaceDetService', file=DESCRIPTOR, index=0, serialized_options=None, create_key=_descriptor._internal_create_key, serialized_start=252, serialized_end=344, methods=[ _descriptor.MethodDescriptor( name='predict', full_name='FaceDetection.FaceDetService.predict', index=0, containing_service=None, input_type=_FACEDETREQUEST, output_type=_FACEDETRESPONSE, serialized_options=None, create_key=_descriptor._internal_create_key, ), ]) _sym_db.RegisterServiceDescriptor(_FACEDETSERVICE) DESCRIPTOR.services_by_name['FaceDetService'] = _FACEDETSERVICE # @@protoc_insertion_point(module_scope)
<filename>tests/tests_semantic/test_scripts.py<gh_stars>1-10 #!/usr/bin/env python # -*- coding: utf-8 -*- # This file was part of Flask-Bootstrap and was modified under the terms of # its BSD License. Copyright (c) 2013, <NAME>. All rights reserved. # # This file was part of Bootstrap-Flask and was modified under the terms of # its MIT License. Copyright (c) 2018 <NAME>. All rights reserved. # # This file is part of the # Flask-SemanticUI Project (https://github.com/juniors90/Flask-SemanticUI/). # Copyright (c) 2021, <NAME> # License: MIT # Full Text: https://github.com/juniors90/Flask-SemanticUI/blob/master/LICENSE from flask_semanticui import ( link_css_with_sri, scripts_with_sri, simple_link_css, simple_scripts_js, ) def test_link_css(): css_html_sri = ( '<link rel="stylesheet" href="https://cdn.jsdelivr.net/' + 'npm/semantic-ui@2.4.2/dist/semantic.min.css" ' + 'integrity="sha256-UXesixbeLkB/UYxVTzuj/gg3+LMzgwAmg3zD+C4ZASQ=" ' + 'crossorigin="anonymous">' ) css_sri = "sha256-UXesixbeLkB/UYxVTzuj/gg3+LMzgwAmg3zD+C4ZASQ=" css_url = ( "https://cdn.jsdelivr.net/npm/semantic-ui@2.4.2/dist/semantic.min.css" ) css = ( '<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm' + '/semantic-ui@2.4.2/dist/semantic.min.css">' ) assert css == simple_link_css(css_url) assert css_sri in link_css_with_sri(css_url, css_sri) assert css_url in link_css_with_sri(css_url, css_sri) assert css_html_sri == link_css_with_sri(css_url, css_sri) def test_simple_link_css_js(): js_html_sri = ( '<script src="https://cdn.jsdelivr.net/npm/' + 'semantic-ui@2.4.2/dist/semantic.min.js" ' + 'integrity="<KEY> ' + 'crossorigin="anonymous"></script>' ) js_sri = "<KEY> js_url = ( "https://cdn.jsdelivr.net/npm/semantic-ui@2.4.2/dist/semantic.min.js" ) js = ( '<script src="https://cdn.jsdelivr.net/' + 'npm/semantic-ui@2.4.2/dist/semantic.min.js"></script>' ) assert js == simple_scripts_js(js_url) assert js_sri in scripts_with_sri(js_url, js_sri) assert js_url in scripts_with_sri(js_url, js_sri) assert js_html_sri == scripts_with_sri(js_url, js_sri) def test_semantic_find_local_resource(app, semantic): with app.app_context(), app.test_request_context(): app.config["SEMANTIC_SERVE_LOCAL"] = True app.config["SERVER_NAME"] = "localhost" url_css = semantic.load_css() url_js_and_jquery = semantic.load_js() css = ( '<link rel="stylesheet" type="text/css" ' + 'href="/static/css/semantic.min.css">' ) js = '<script src="/static/js/semantic/semantic.min.js"></script>' jquery = '<script src="/static/js/semantic/jquery.min.js"></script>' assert css in url_css assert js in url_js_and_jquery assert jquery in url_js_and_jquery def test_semantic_find_cdn_resource(app, semantic): with app.app_context(), app.test_request_context(): app.config["SEMANTIC_SERVE_LOCAL"] = False url_css = semantic.load_css() url_js_and_jquery = semantic.load_js() css = ( '<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/' + 'semantic-ui@2.4.2/dist/semantic.min.css" ' + 'integrity="<KEY> + ' crossorigin="anonymous">' ) js = ( '<script src="https://cdn.jsdelivr.net/npm/' + 'semantic-ui@2.4.2/dist/semantic.min.js" ' + 'integrity="<KEY> ' + 'crossorigin="anonymous"></script>' ) jquery = ( '<script src="https://cdn.jsdelivr.net/npm/' + 'jquery@3.1.1/dist/jquery.min.js" ' + 'integrity="<KEY> ' + 'crossorigin="anonymous"></script>' ) assert css in url_css assert js in url_js_and_jquery assert jquery in url_js_and_jquery
<filename>src/main.py import numpy as np import pandas as pd import math from typing import Sequence import argparse def makeA(c: list): return -np.matrix([ [1, 1, 0, c[0]], [1, 1, 0, - c[1]], [1, 0, 1, c[2]], [1, 0, 1, - c[3]], [1, -1, 0, c[4]], [1, -1, 0, - c[5]], [1, 0, -1, c[6]], [1, 0, -1, - c[7]], ]) def makeC(c: list): c0 = sum([c[j]*((-1)**j)for j in range(len(c))]) c1 = c[0]-c[1]-c[4]+c[5] c2 = c[2]-c[3]-c[6]+c[7] return np.matrix([ [8, 0, 0, c0], [0, 4, 0, c1], [0, 0, 4, c2], [c0, c1, c2, sum([i ** 2 for i in c])], ]) def makeOmega(theta: list): return np.matrix([ [sum(theta) - 2 * math.pi], [theta[0] + theta[1] - theta[4] - theta[5]], [theta[2] + theta[3] - theta[6] - theta[7]], [sum([((-1)**j)*math.log(math.sin(theta[j])) for j in range(len(theta))])] ]) # 繰り返す函数 def calcResidual(theta: np.matrix): c = np.matrix([1.0/math.tan(*value) for value in theta.tolist()]).T A = makeA(*c.T.tolist()) omega = makeOmega(*theta.T.tolist()) C = makeC(*c.T.tolist()) return A * (C ** -1) * omega # 収束判定 def checkConvergence(residuals: np.matrix): if (len(residuals) == 0): return False value = max([max([math.fabs(residual) for residual in row]) for row in residuals]) return value < 10.0**-9 def todegree(radian: float, digit: int = 10): degrees = math.degrees(radian) degf, deg = math.modf(degrees) minutes = int(degf*60) seconds = int(degf*3600) % 60+degf*3600-int(degf*3600) return f'{int(deg)}°{int(minutes)}′{seconds:.{digit}g}′′' def printAngles(angles: Sequence[float], indent: int = 1, symbol: str = 'M'): space = ''.join(['\t']*indent) for i in range(len(angles)): print(f'{space}{symbol}_{i} = {todegree(angles[i],4)}') def printTheta(angles: Sequence[float], step: int, indent: int = 1, symbol: str = 'θ'): space = ''.join(['\t']*indent) for i in range(len(angles)): print(f'{space}{symbol}_{i}({step}) = {todegree(angles[i],4)}') # main函数 if __name__ == '__main__': # commald-line argumentsの設定 parser = argparse.ArgumentParser( description='三角測量によって得た四辺形鎖の8個の角(radian)の最確値と残差を求めるscript') parser.add_argument('input_file', help='角の値が書き込まれたcsvファイル') parser.add_argument('--ignore', '-I', choices=[ 'header', 'index', 'both'], default='none', help='csv fileのheader及びindexを無視するかどうか') args = parser.parse_args() df=None # fileを読み込む if args.ignore == 'header': df = pd.read_csv(args.input_file, usecols=[0]) elif args.ignore == 'index': df = pd.read_csv(args.input_file, index_col=0, header=None, usecols=[0, 1]) elif args.ignore == 'both': df = pd.read_csv(args.input_file, index_col=0, usecols=[0, 1]) else: df = pd.read_csv(args.input_file, header=None, usecols=[0]) # 初期値 measured_angles = np.matrix(df, dtype=float) theta = np.matrix(measured_angles); i = 0 print('This program calculates angles in Radian') print('Measured angles:') printTheta(*theta.T.tolist(), i, 1) print('Start calculating...') # 残差を保持するlist residuals = [] # 計算処理 while not checkConvergence(residuals): residual = calcResidual(theta) residuals.append(*residual.T.tolist()) theta += residual i += 1 print(f'### result of step {i} ###') printTheta(*theta.T.tolist(), i) printTheta(residual, i-1, symbol='⊿') if len(residuals) > 10: residuals.pop(0) print(f'##########################') print('Finish calculating!') print('Most probable angles:') printAngles(*theta.T.tolist()) print('Residuals:') printAngles(*(theta - measured_angles).T.tolist(), symbol='ν')
<filename>tests/conftest.py import pytest from ws_rebalancer.wealthsimple_login import WealthSimpleLogin class WealthSimpleLoginMock: """Mocks the WealthSimpleLogin class which provides the WSTrade API. This allows us to mock out the API calls we make in the app so that we don't actually make calls to the WealthSimple Trade API backend as that would get us rate-limited pretty quickly, or even blocke if they think we're DDOSing them. """ def __init__(self, email, password, two_factor_callback=None): self._accounts = {} self._positions = {} self._securities = [] # build the dict containing info on the positions held by the client # in the tests if hasattr(self, 'test_positions'): for account_id, account in self.test_positions.items(): self._positions[account_id] = [] buying_power = account['buying_power'] self._accounts[account_id] = { 'buying_power': {'amount': buying_power}} for ticker, position in account['positions'].items(): position_dict = {'stock': {}, 'quote': {}} position_dict['stock']['symbol'] = ticker position_dict['quantity'] = position['qty'] position_dict['quote']['amount'] = position['price'] self._positions[account_id].append(position_dict) # build the dict containing info on the securities used in the tests if hasattr(self, 'test_securities'): for ticker, security_info in self.test_securities.items(): security_dict = {'id': None, 'stock': {}, 'quote': {}} security_dict['id'] = security_info['id'] security_dict['stock']['symbol'] = ticker security_dict['stock']['name'] = security_info['name'] security_dict['stock']['primary_exchange'] = ( security_info['exchange']) security_dict['quote']['amount'] = security_info['price'] self._securities.append(security_dict) # simulate calling the sample two_factor_auth function two_factor_callback() def get_account_ids(self): return list(self._accounts.keys()) def get_account(self, account_id): return self._accounts[account_id] def get_positions(self, account_id): return self._positions[account_id] def get_securities_from_ticker(self, ticker): securities = [] for security in self._securities: if ticker in security['stock']['symbol']: securities.append(security) return securities def get_security(self, security_id): for security in self._securities: if security['id'] == security_id: return security @pytest.fixture(scope="session") def testfiles_dir(tmpdir_factory): return tmpdir_factory.mktemp("testfiles") @pytest.fixture(scope="session", autouse=True) def wslogin_base_patch(): # We need to manually set the base class of WealthSimpleLogin class to be # the mock class because otherwise it will keep using the WSTrade class # even after patching WealthSimpleLogin.__bases__ = (WealthSimpleLoginMock,) @pytest.fixture(scope='function') def wslogin_mock(mocker): mock_obj = mocker.patch( 'ws_rebalancer.wealthsimple_login.wealthsimple.WSTrade', new=WealthSimpleLoginMock) mock_obj.test_positions = {} mock_obj.test_securities = {} yield mock_obj # Everything past this point occurs once the test using this fixture has # completed. Delete any class attributes we may have set in our tests if hasattr(mock_obj, 'test_positions'): del mock_obj.test_positions if hasattr(mock_obj, 'test_securities'): del mock_obj.test_securities
import requests from typing import List, Dict from data_refinery_common.models import ( Batch, File, SurveyJobKeyValue, Organism ) from data_refinery_foreman.surveyor import utils from data_refinery_foreman.surveyor.external_source import ExternalSourceSurveyor from data_refinery_common.job_lookup import ProcessorPipeline, Downloaders from data_refinery_common.logging import get_and_configure_logger logger = get_and_configure_logger(__name__) EXPERIMENTS_URL = "https://www.ebi.ac.uk/arrayexpress/json/v3/experiments/" SAMPLES_URL = EXPERIMENTS_URL + "{}/samples" class ArrayExpressSurveyor(ExternalSourceSurveyor): def source_type(self): return Downloaders.ARRAY_EXPRESS.value def determine_pipeline(self, batch: Batch, key_values: Dict = {}) -> ProcessorPipeline: # If it's a CEL file run SCAN.UPC on it. if batch.files[0].raw_format == "CEL": return ProcessorPipeline.AFFY_TO_PCL # If only processed data is available then we don't need to do # anything to it elif batch.files[0].raw_format == batch.files[0].processed_format: return ProcessorPipeline.NO_OP # If it's not CEL and it's not already processed then we just # want to download it for Jackie's grant. else: return ProcessorPipeline.NONE def group_batches(self) -> List[List[Batch]]: return utils.group_batches_by_first_file(self.batches) def get_experiment_metadata(self, experiment_accession_code: str) -> Dict: experiment_request = requests.get(EXPERIMENTS_URL + experiment_accession_code) parsed_json = experiment_request.json()["experiments"]["experiment"][0] experiment = {} experiment["name"] = parsed_json["name"] experiment["experiment_accession_code"] = experiment_accession_code # If there is more than one arraydesign listed in the experiment # then there is no other way to determine which array was used # for which sample other than looking at the header of the CEL # file. That obviously cannot happen until the CEL file has been # downloaded so we can just mark it as UNKNOWN and let the # downloader inspect the downloaded file to determine the # array then. if len(parsed_json["arraydesign"]) == 0: logger.warn("Experiment %s has no arraydesign listed.", experiment_accession_code, survey_job=self.survey_job.id) experiment["platform_accession_code"] = "UNKNOWN" elif len(parsed_json["arraydesign"]) > 1: experiment["platform_accession_code"] = "UNKNOWN" else: experiment["platform_accession_code"] = \ parsed_json["arraydesign"][0]["accession"] experiment["release_date"] = parsed_json["releasedate"] if "lastupdatedate" in parsed_json: experiment["last_update_date"] = parsed_json["lastupdatedate"] else: experiment["last_update_date"] = parsed_json["releasedate"] return experiment def _generate_batches(self, samples: List[Dict], experiment: Dict, replicate_raw: bool = True) -> List[Batch]: """Generates a Batch for each sample in samples. Uses the metadata contained in experiment (which should be generated via get_experiment_metadata) to add additional metadata to each Batch. If replicate_raw is True (the default) then only raw files will be replicated. Otherwise all files will be replicated. """ for sample in samples: if "file" not in sample: continue organism_name = "UNKNOWN" for characteristic in sample["characteristic"]: if characteristic["category"].upper() == "ORGANISM": organism_name = characteristic["value"].upper() if organism_name == "UNKNOWN": logger.error("Sample from experiment %s did not specify the organism name.", experiment["experiment_accession_code"], survey_job=self.survey_job.id) organism_id = 0 else: organism_id = Organism.get_id_for_name(organism_name) for sample_file in sample["file"]: # Generally we only want to replicate the raw data if # we can, however if there isn't raw data then we'll # take the processed stuff. if (replicate_raw and sample_file["type"] != "data") \ or sample_file["name"] is None: continue # sample_file["comment"] is only a list if there's # more than one comment... comments = sample_file["comment"] if isinstance(comments, list): # Could be: "Derived ArrayExpress Data Matrix FTP # file" or: "ArrayExpress FTP file". If there is # no comment with a name including "FTP file" then # we don't know where to download it so we need to # mark this job as an error. Therefore don't catch # the potential exception where download_url # doesn't get defined. for comment in comments: if comment["name"].find("FTP file") != -1: download_url = comment["value"] else: download_url = comments["value"] raw_format = sample_file["name"].split(".")[-1] processed_format = "PCL" if replicate_raw else raw_format file = File(name=sample_file["name"], download_url=download_url, raw_format=raw_format, processed_format=processed_format, size_in_bytes=-1) # Will have to be determined later self.add_batch(platform_accession_code=experiment["platform_accession_code"], experiment_accession_code=experiment["experiment_accession_code"], organism_id=organism_id, organism_name=organism_name, experiment_title=experiment["name"], release_date=experiment["release_date"], last_uploaded_date=experiment["last_update_date"], files=[file]) def discover_batches(self): experiment_accession_code = ( SurveyJobKeyValue .objects .get(survey_job_id=self.survey_job.id, key__exact="experiment_accession_code") .value ) logger.info("Surveying experiment with accession code: %s.", experiment_accession_code, survey_job=self.survey_job.id) experiment = self.get_experiment_metadata(experiment_accession_code) r = requests.get(SAMPLES_URL.format(experiment_accession_code)) samples = r.json()["experiment"]["sample"] self._generate_batches(samples, experiment) if len(samples) != 0 and len(self.batches) == 0: # Found no samples with raw data, so replicate the # processed data instead self._generate_batches(samples, experiment, replicate_raw=False)
<reponame>mumupy/mmdeeplearning<filename>src/mtensorflow/tf_bpn.py #!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2019/10/24 9:03 # @Author : ganliang # @File : tf_bpn.py # @Desc : tensorflow反向传播 import os import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import matplotlib.pyplot as plt from src.config import logger, root_path def _sigmaprime(X): return tf.multiply(tf.sigmoid(X), tf.subtract(tf.constant(1.0), tf.sigmoid(X))) def _multilayer_perceptron(X, weights, biases): h_layer_1 = tf.add(tf.matmul(X, weights["h1"]), biases["h1"]) out_layer_1 = tf.sigmoid(h_layer_1) h_out = tf.matmul(out_layer_1, weights["out"]) + biases["out"] return tf.sigmoid(h_out), h_out, out_layer_1, h_layer_1 def bpn(epochs=10, batch_size=1000, learning_rate=0.01, hidden=30): """ 反向传播实现 :return: """ mnist_path = os.path.join(root_path, "data", "fashionMNIST") mnist_data = input_data.read_data_sets(mnist_path, one_hot=True) train_data = mnist_data.train test_data = mnist_data.test sample_count = train_data.num_examples feature_count = train_data.images.shape[1] label_count = train_data.labels.shape[1] X = tf.placeholder(tf.float32, shape=(None, feature_count)) Y = tf.placeholder(tf.float32, shape=(None, label_count)) weights = { "h1": tf.Variable(tf.random_normal(shape=(feature_count, hidden), seed=0)), "out": tf.Variable(tf.random_normal(shape=(hidden, label_count), seed=0)) } baises = { "h1": tf.Variable(tf.random_normal(shape=(1, hidden), seed=0)), "out": tf.Variable(tf.random_normal(shape=(1, label_count), seed=0)) } Y_that, h_out, out_layer_1, h_layer_1 = _multilayer_perceptron(X, weights, baises) # 反向传播 error = Y_that - Y delta_2 = tf.multiply(error, _sigmaprime(h_out)) delta_w_2 = tf.matmul(tf.transpose(out_layer_1), delta_2) wtd_error = tf.matmul(delta_2, tf.transpose(weights["out"])) delta_1 = tf.multiply(wtd_error, _sigmaprime(h_layer_1)) delta_w_1 = tf.matmul(tf.transpose(X), delta_1) eta = tf.constant(learning_rate) step = [tf.assign(weights["h1"], tf.subtract(weights["h1"], tf.multiply(eta, delta_w_1))), tf.assign(baises["h1"], tf.subtract(baises["h1"], tf.multiply(eta, tf.reduce_mean(delta_1, axis=[0])))), tf.assign(weights["out"], tf.subtract(weights["out"], tf.multiply(eta, delta_w_2))), tf.assign(baises["out"], tf.subtract(baises["out"], tf.multiply(eta, tf.reduce_mean(delta_2, axis=[0]))))] acct_mat = tf.equal(tf.argmax(Y_that, 1), tf.argmax(Y, 1)) with tf.name_scope("accuracy"): accuracy = tf.reduce_mean(tf.cast(acct_mat, tf.float32)) tf.summary.scalar("accuracy", accuracy) init = tf.global_variables_initializer() summary_ops = tf.summary.merge_all() acc_trains, acc_tests = [], [] with tf.Session() as sess: sess.run(init) writer = tf.summary.FileWriter("bpn", graph=sess.graph) for i in range(epochs): batch_count = sample_count // batch_size for j in range(batch_count): batch_trains, batch_lables = mnist_data.train.next_batch(batch_size) _, summary_str = sess.run([step, summary_ops], feed_dict={X: batch_trains, Y: batch_lables}) writer.add_summary(summary_str, i * batch_size + j) # 训练数据评估值 acc_train = sess.run(accuracy, feed_dict={X: train_data.images, Y: train_data.labels}) # 测试数据评估值 acc_test = sess.run(accuracy, feed_dict={X: test_data.images, Y: test_data.labels}) logger.info("epoll {0} train accuracy {1} test accuracy {2}".format(i, acc_train, acc_test)) acc_trains.append(acc_train) acc_tests.append(acc_test) writer.close() plt.plot(list(range(epochs)), acc_trains, "bo", label="train accuracy") plt.plot(list(range(epochs)), acc_tests, "r", label="test accuracy") plt.xlabel("epoch") plt.xlabel("accuracy") plt.title("accuracy train/test") plt.legend() plt.show()
<filename>tests/test_utils/test_solver.py<gh_stars>0 import numpy as np from summer_py.summer_model.utils.solver import solve_with_euler, solve_with_rk4 def test_solve_with_rk4_linear_func(): """ Ensure Runge-Kutta 4 method can solve a linear function's ODE. y_0 = 2 * t y_1 = 1 * t dy_0/dt = 2 dy_1/dt = 1 """ def ode_func(vals, time): return np.array([2, 1]) values = np.array([0, 0]) times = np.array([0, 1, 2]) output_arr = solve_with_rk4(ode_func, values, times, solver_args={"step_size": 0.5}) expected_outputs = [ # t = 0 [0, 0], # t = 1 [2, 1], # t = 2 [4, 2], ] equals_arr = np.array(expected_outputs) == output_arr assert equals_arr.all() def test_solve_with_rk4_quadratic_func(): """ Ensure Runge-Kutta 4 method can solve a quadratic function's ODE. y_0 = t ** 2 y_1 = t ** 2 + t y_2 = 2 * t ** 2 dy_0/dt = 2 * t dy_1/dt = 2 * t + 1 dy_2/dt = 4 * t """ def ode_func(vals, time): return np.array([2 * time, 2 * time + 1, 4 * time]) values = np.array([0, 1, 2]) times = np.array([0, 1, 2, 3]) output_arr = solve_with_rk4(ode_func, values, times, solver_args={"step_size": 0.01}) expected_outputs = [ # t = 0 [0 ** 2, 0 ** 2 + 0 + 1, 2 * 0 ** 2 + 2], # t = 1 [1 ** 2, 1 ** 2 + 1 + 1, 2 * 1 ** 2 + 2], # t = 2 [2 ** 2, 2 ** 2 + 2 + 1, 2 * 2 ** 2 + 2], # t = 3 [3 ** 2, 3 ** 2 + 3 + 1, 2 * 3 ** 2 + 2], ] tolerance = 0.001 equals_arr = np.array(expected_outputs) - output_arr < tolerance assert equals_arr.all() def test_solve_with_euler_linear_func(): """ Ensure Euler method can solve a linear function's ODE. y_0 = 2 * t y_1 = 1 * t dy_0/dt = 2 dy_1/dt = 1 """ def ode_func(vals, time): return np.array([2, 1]) values = np.array([0, 0]) times = np.array([0, 1, 2]) output_arr = solve_with_euler(ode_func, values, times, solver_args={"step_size": 0.5}) expected_outputs = [ # t = 0 [0, 0], # t = 1 [2, 1], # t = 2 [4, 2], ] equals_arr = np.array(expected_outputs) == output_arr assert equals_arr.all() def test_solve_with_euler_quadratic_func(): """ Ensure Euler method can solve a quadratic function's ODE. y_0 = t ** 2 y_1 = t ** 2 + t y_2 = 2 * t ** 2 dy_0/dt = 2 * t dy_1/dt = 2 * t + 1 dy_2/dt = 4 * t """ def ode_func(vals, time): return np.array([2 * time, 2 * time + 1, 4 * time]) values = np.array([0, 1, 2]) times = np.array([0, 1, 2, 3]) output_arr = solve_with_euler(ode_func, values, times, solver_args={"step_size": 0.01}) expected_outputs = [ # t = 0 [0 ** 2, 0 ** 2 + 0 + 1, 2 * 0 ** 2 + 2], # t = 1 [1 ** 2, 1 ** 2 + 1 + 1, 2 * 1 ** 2 + 2], # t = 2 [2 ** 2, 2 ** 2 + 2 + 1, 2 * 2 ** 2 + 2], # t = 3 [3 ** 2, 3 ** 2 + 3 + 1, 2 * 3 ** 2 + 2], ] tolerance = 0.1 equals_arr = np.array(expected_outputs) - output_arr < tolerance assert equals_arr.all()
<filename>NonSomeFinder/src/NonSomeFinder.py ''' Created on Nov 14, 2014 Copied from GitHubResearchDataMiner @author: bgt ''' import ConfigParser import os import sys import time import CsvDao import GitHubDao from GitHubFacadeForProcessDistibutionDao import GitHubFacadeForProcessDistibutionDao from github.GithubException import GithubException import ProcessDistributionDao from BgtConfiguration import BgtConfiguration from BgtConfiguration import BadCommandLineException def analyseRepositories(hits): countDooku = 0 for repo in hits: countDooku += 1 try: print "Analysing repository #"+str(countDooku)+", "+repo.full_name+" "+str(repo.id) if config.searching == "facebook": analysis = connection.usesFacebookGraph(repo) else: analysis = connection.usesSsl(repo) csvDao.addRow(analysis) except GithubException: print "...except that repository has blocked access" csvDao.close() def delegateRepositories(config, hits): minions = ProcessDistributionDao.ProcessDistributionDao(config, True) for repo in hits: minions.pushToDelegationFile(str(repo.full_name)+','+str(repo.id)) ''' Given the duration of runtime as seconds, this method will print out the duration as hours, minutes and seconds. ''' def announceRunTimeFromSeconds(seconds): remains = int(seconds) actualSeconds = remains-60*int(remains/60) #remove full minutes and bigger remains -= actualSeconds actualMinutes = int((remains-3600*int(remains/3600))/60) #remove full hours and covert seconds to minutes remains -= actualMinutes*60 actualHours = int(remains/21600) #convert seconds to hours outputString = "Run time:" if actualHours>0: outputString += " "+str(actualHours)+" hours" if actualMinutes>0: outputString += " "+str(actualMinutes)+" minutes" if actualSeconds>0: outputString += " "+str(actualSeconds)+" seconds ("+str(seconds)+" total seconds)" print outputString def printHowToUse(): print "Usage: python NonSomeFinder.py [-ssl|-facebook] [-issues] -search searchword outputfile.csv" print "Usage: python NonSomeFinder.py [-ssl|-facebook] [-issues] -search searchword [-delegate delegationfile1[,delegationfile2[...]]" print "Usage: python NonSomeFinder.py -facebook [-since #id] outputfile.csv" print "Usage: python NonSomeFinder.py -facebook [-since #id] [-delegate delegationfile1[,delegationfile2[...]]" print "Usage: python NonSomeFinder.py -ssl|-facebook -takeover delegationfile outputfile.csv" if __name__ == '__main__': config = BgtConfiguration() try: config.readConfigfile(os.path.dirname(__file__)+'/config.cfg') config.parseCommandLine(sys.argv) except BadCommandLineException as e: print e.message printHowToUse() sys.exit() #Ready to start working! if config.search != "": print "Looking for "+config.search connection = GitHubDao.GitHubDao(config) if config.outputfile != "": csvDao = CsvDao.CsvDao(config.outputfile) startTime = int(time.time()) if config.delegation == "takeover": #We will either analyse repositories from a delegation file... hits = GitHubFacadeForProcessDistibutionDao(config, connection) elif config.search == "": #...or look through all repositories... hits = connection.findAllRepositories(config.sinceid) elif config.lookinto == "reponame": #...or seek by the names of the repositories... hits = connection.findRepositoryNamesWithSearchPhrase(config.search) else: #...or seek for repositories with keyword in issues. hits = connection.findRepositoryIssuesWithSearchPhrase(config.search) if config.delegation == "delegate": #Delegate forth the analysing job delegateRepositories(config, hits) else: #The search result pipe is given as a parameter analyseRepositories(hits) endTime = int(time.time()) announceRunTimeFromSeconds(endTime-startTime)
from tqdm import tqdm import argparse import pickle import numpy as np import collections import json import operator import torch from random import shuffle import gc import jsonlines import os import sys import operator import random random.seed(42) np.random.seed(42) def create_social_ranking(fname,scorefname,outname): with jsonlines.open(fname) as datafile, jsonlines.open(scorefname) as scorefile, jsonlines.open(outname,mode="w") as writer: for row,scores in tqdm(zip(datafile,scorefile),desc="Writing File"): idx = row["id"] fact = row["fact"] passage = row['passage'] score = scores['score'] choices = row['choices'] choice_str = ' | '.join(choices) premise = passage + " . " + choice_str writer.write({"idx":idx,"premise":premise,"hypo":fact,"label":score}) def create_social_ranking_scaled(fname,scorefname,outname,scale=True,topk=None): with jsonlines.open(fname) as datafile, jsonlines.open(scorefname) as scorefile, jsonlines.open(outname,mode="w") as writer: row_scores = {} for row,scores in tqdm(zip(datafile,scorefile),desc="Merging File"): idx = row["id"] fact = row["fact"] passage = row['passage'] score = scores['score'] choices = row['choices'] choice_str = ' | '.join(choices) premise = passage + " . " + choice_str ix = idx.split(":")[0] if ix not in row_scores: row_scores[ix]={} row_scores[ix]['row'] = {"id":ix,"premise":premise} row_scores[ix]['facts']= [] row_scores[ix]['facts'].append([fact,score]) for ix,data in tqdm(row_scores.items(),desc="Scaling Scores"): facts = data["facts"] if topk is None: topk=len(facts) sorted_facts = list(sorted(facts, key=operator.itemgetter(1),reverse=True))[0:topk] if scale: max_val = sorted_facts[0][1] min_val = sorted_facts[-1][1] scaled_facts = [] for tup in sorted_facts: tup[1] = (tup[1]-min_val)/(max_val-min_val) scaled_facts.append(tup) sorted_facts=scaled_facts data["facts"]=sorted_facts row_scores[ix]=data for ix,data in tqdm(row_scores.items(),desc="Writing Scores"): premise = data['row']["premise"] for fix,tup in enumerate(data["facts"]): idx = ix+":"+str(fix) fact = tup[0] score = tup[1] writer.write({"idx":idx,"premise":premise,"hypo":fact,"label":score}) def convert_to_label(facts,label=1,scaled=False): if not scaled: return [ (tup[0],label) for tup in facts ] else: return [ (tup[0],tup[1]) for tup in facts ] def get_facts_labels(facts): fs= [ tup[0] for tup in facts] labels = [ tup[1] for tup in facts] return fs,labels def create_social_mcml_scaled(fname,scorefname,outname,scale=True,topk=None): with jsonlines.open(fname) as datafile, jsonlines.open(scorefname) as scorefile, jsonlines.open(outname,mode="w") as writer: row_scores = {} for row,scores in tqdm(zip(datafile,scorefile),desc="Merging File"): idx = row["id"] fact = row["fact"] passage = row['passage'] score = scores['score'] choices = row['choices'] choice_str = ' | '.join(choices) premise = passage + " . " + choice_str ix = idx.split(":")[0] if ix not in row_scores: row_scores[ix]={} row_scores[ix]['row'] = {"id":ix,"premise":premise} row_scores[ix]['facts']= [] row_scores[ix]['facts'].append([fact,score]) for ix,data in tqdm(row_scores.items(),desc="Scaling Scores"): facts = data["facts"] if topk is None: topk=len(facts) sorted_facts = list(sorted(facts, key=operator.itemgetter(1),reverse=True))[0:topk] if scale: max_val = sorted_facts[0][1] min_val = sorted_facts[-1][1] scaled_facts = [] for tup in sorted_facts: tup[1] = (tup[1]-min_val)/(max_val-min_val) scaled_facts.append(tup) sorted_facts=scaled_facts data["facts"]=sorted_facts row_scores[ix]=data min_len = 100 max_len = 0 skipped = 0 all_skipped = 0 mp_sk = {} for ix,data in tqdm(row_scores.items(),desc="Writing Scores"): premise = data['row']["premise"] # top9 = convert_to_label(data["facts"][0:9],label=1) # rest9s = data["facts"][9:] # shuffle(rest9s) # rest9s=rest9s[0:27] # wrong9 = convert_to_label(rest9s[0:9],label=0) # wrong18 = convert_to_label(rest9s[9:18],label=0) # wrong27 = convert_to_label(rest9s[18:],label=0) # s1 = top9+wrong9 # s2 = top9+wrong18 # s3 = top9+wrong27 top15 = convert_to_label(data["facts"][0:15],label=1,scaled=scale) if len(data["facts"])<=30: randfn = random.choices else: randfn = random.sample wrong15_1 = convert_to_label(randfn(data["facts"][15:],k=15),label=0,scaled=scale) wrong15_2 = convert_to_label(randfn(data["facts"][15:],k=15),label=0,scaled=scale) wrong15_3 = convert_to_label(randfn(data["facts"][15:],k=15),label=0,scaled=scale) s1 = top15+wrong15_1 s2 = top15+wrong15_2 s3 = top15+wrong15_3 min_len = min([min_len,len(s1),len(s2),len(s3)]) max_len = max([max_len,len(s1),len(s2),len(s3)]) sk_cur = 0 for nix,ss in enumerate([s1,s2,s3]): if len(ss) < 30: skipped+=1 sk_cur+=1 continue shuffle(ss) fs,labels = get_facts_labels(ss) if scale: min_val,max_val = min(labels),max(labels) den = max_val-min_val labels= [ (label-min_val)/den for label in labels] writer.write({"idx":ix+":"+str(nix),"premise":premise,"choices":fs,"labels":labels}) mp_sk[sk_cur] = mp_sk.get(sk_cur,0)+1 if sk_cur==3: all_skipped+=1 print(f"Lengths is: {min_len,max_len}") print(f"Skipped : {skipped}") print(f"All Skipped : {all_skipped,mp_sk}") if __name__ == "__main__": typet = sys.argv[1] fname = sys.argv[2] scorefname = sys.argv[3] outname = sys.argv[4] topk = None if len(sys.argv)>5: topk = int(sys.argv[5]) if typet == "social": create_social_ranking(fname,scorefname,outname) elif typet == "social_scaled": create_social_ranking_scaled(fname,scorefname,outname,scale=True,topk=topk) elif typet == "social_unscaled": create_social_ranking_scaled(fname,scorefname,outname,scale=False,topk=topk) elif typet == "social_mcml": create_social_mcml_scaled(fname,scorefname,outname,scale=True)
# -*- coding: utf-8 -*- # Loading libraries import os import sys import time from networkx.algorithms.centrality import group import pandas as pd import re import csv from swmmtoolbox import swmmtoolbox as swmm from datetime import datetime from os import listdir from concurrent import futures from sqlalchemy import create_engine from sqlalchemy.orm import scoped_session from sqlalchemy.orm import sessionmaker import multiprocessing import pyproj event_id = input('4-digit event_id like: 0123: ') model_id = f'model_{event_id}'# + input('4-digit model_id like: 0123: ' ) precipitation_id = f'precipitation_{event_id}'# + input('4-digit raingage_id like 0123: ') epsg_modelo = input('EPSG (ejemplo: 5348): ') project_folder = os.path.abspath(os.path.join(os.getcwd(),"../..")) data_raw_folder = os.path.join(project_folder,'data', 'raw_swmm') event_folder = os.path.join(data_raw_folder, 'Run_[' + event_id + ']') model_inp = os.path.join(event_folder, 'model.inp') model_out = os.path.join(event_folder, 'model.out') # Connection to database engine_base_ina = create_engine('postgresql://postgres:postgres@172.18.0.1:5555/base-ina') RELEVANT_GROUP_TYPES_OUT = [ 'link', 'node', 'subcatchment', # 'system' ] RELEVANT_GROUP_TYPES_INP = [ 'coordinates', 'subcatchments', 'raingages', 'conduits', 'orifices', 'weirs', 'outfalls', # 'vertices', # 'polygons', 'subareas', # 'losses', 'infiltration', 'junctions', 'storage', # 'properties', # "curves", ] RELEVANT_LINKS = [ # 'channel10944', # 'channel24416', # 'channel60443', # 'channel17459', # 'channel87859', # 'channel14380', # 'channel55414', # 'channel77496', # 'channel83013', # 'channel52767', # 'channel12818', # 'conduit11698', # 'channel6317', # 'conduit18801', # 'conduit50317', # 'conduit528', # 'conduit36611', # 'conduit50827', # 'conduit78108', # 'conduit57848', # 'conduit42638', # 'conduit34157', # 'conduit29340', # 'conduit19715', # 'conduit23023', # 'conduit37130', # 'conduit21772', # 'channel52598', # 'conduit75783', # 'conduit62715', # 'conduit48979', # 'conduit82544', # 'conduit83110', # 'conduit33678', # 'conduit18303', # 'conduit40724', # 'conduit13927' ] RELEVANT_SUBCATCHMENTS = [] RELEVANT_NODES = [] RELEVANT_SUBAREAS = [] RELEVANT_OUTFALLS = [] RELEVANT_VERTICES = [] RELEVANT_POLYGNOS = [] RELEVANT_LINKS_CONDUITS = [] RELEVANT_LINKS_ORIFICES = [] RELEVANT_LINKS_WEIRS = [] RELEVANT_LOSSES = [] RELEVANT_INFILTRATION = [] RELEVANT_JUNCTIONS = [] RELEVANT_STORAGE = [] MODEL_OUT_COLS = { 'SUBCATCHMENTS_COLS' : [ 'event_id', 'elapsed_time', 'subcatchment_id', 'rainfall', 'elapsed_time', 'snow_depth', 'evaporation_loss', 'infiltration_loss', 'runoff_rate', 'groundwater_outflow', 'groundwater_elevation', 'soil_moisture' ], 'LINKS_COLS' : [ 'event_id', 'elapsed_time', 'link_id', 'flow_rate', 'flow_depth', 'flow_velocity', 'froude_number', 'capacity' ], 'NODES_COLS' : [ 'event_id', 'elapsed_time', 'node_id', 'depth_above_invert', 'hydraulic_head', 'volume_stored_ponded', 'lateral_inflow', 'total_inflow', 'flow_lost_flooding' ] } MODEL_INP_COLS = { 'NODES_COORDINATES' : [ 'node_id', 'x_coord', 'y_coord', ], "SUBCATCHMENTS" : [ "subcatchment_id", "raingage_id", "outlet", "area", "imperv", "width", "slope", "curb_len" ], "LINKS_CONDUITS" : [ "conduit_id", "from_node", "to_node", "length", "roughness", "in_offset", "out_offset", "init_flow", "max_flow" ], "LINKS_ORIFICES" : [ "orifice_id", "from_node", "to_node", "type", "offset", "q_coeff", "gated", "close_time" ], "LINKS_WEIRS" : [ "weir_id", "from_node", "to_node", "type", "crest_ht", "q_coeff", "gated", "end_con", "end_coeff", "surcharge" ], "SUBAREAS" : [ "subcatchment_id", "n_imperv", "n_perv", "s_imperv", "s_perv", "pct_zero", "route_to" ], "NODES_STORAGE" : [ "storage_id", "elevation", "max_depth", "init_depth", "shape", "curve_name_params", "n_a", "f_evap" ], "NODES_OUTFALLS" : [ "outfall_id", "elevation", "type", # "stage_data", "gated", # "route_to" ], "NODES_JUNCTIONS" : [ "junction_id", "elevation", "max_depth", "init_depth", "sur_depth", "aponded" ], "INFILTRATION": [ "subcatchment_id", "max_rate", "min_rate", "decay", "dry_time", "max_infil", ], # "POLYGONS": [ # "subcatchment_id", # "x_coord", # "y_coord" # ], # "VERICES": [ # "link_id", # "x_coord", # "y_coord" # ], "PROPERTIES": [ "model_name", "model_version", "flow_units", "infiltration", "flow_routing", "link_offsets", "min_slope", "allow_ponding", "skip_steady_state", "start_date", "start_time", "report_start_date", "report_start_time", "end_date", "end_time", "sweep_start", "sweep_end", "report_step", "wet_step", "dry_step", "routing_step", "inertial_damping", "normal_flow_limited", "force_main_equation", "variable_step", "lengthening_step", "min_surfarea", "max_trials", "head_tolerance", "sys_flow", "lat_flow_tol", "minimum_step", "threads" ] } # dictionary to store data groups = {} # Definition of starting postiion of each element def group_start_line(model): with open(model, 'r') as inp: groups = {} count = 0 lines = inp.readlines() for line in lines: if ('[' in line) & (']' in line): groups.update({line[1:-2].lower() : {'start': count}}) count += 1 # subselection of elements from MODEL_ELEMENTS groups = {key:value for key,value in groups.items() if key in RELEVANT_GROUP_TYPES_INP} LINK_TYPES = ['orifices', 'conduits', 'weirs'] NODE_TYPES = ['outfalls', 'junctions', 'storage'] for key in [key for key in groups.keys() if key in LINK_TYPES]: groups['links_' + key] = groups.pop(key) for key in [key for key in groups.keys() if key in NODE_TYPES]: groups['nodes_' + key] = groups.pop(key) groups['nodes_coordinates'] = groups.pop('coordinates') return groups # adding header and skip-lines to elements dict def build_groups_dicts(model): groups = group_start_line(model) count = 0 for element, start_dict in groups.items(): start = start_dict['start'] with open(model, 'r') as inp: lines = inp.readlines() for index, line in enumerate(lines): if (index - start == 1) & (';;' in line) & (';;--' not in line): groups[element].update({'header':[col for col in re.split("\s\s+", line[2:-1]) if len(col) > 1]}) elif (index - start == 2) & (';;--------------' in line): groups[element].update({'line_to_skip': index}) elif (index - start == 3): break # some corrrections on header because of mismatches on inp file # groups['properties'].update({'header': MODEL_INP_COLS['PROPERTIES']}) groups['subcatchments'].update({'header': MODEL_INP_COLS['SUBCATCHMENTS']}) groups['subareas'].update({'header': MODEL_INP_COLS['SUBAREAS']}) groups['infiltration'].update({'header': MODEL_INP_COLS['INFILTRATION']}) groups['links_conduits'].update({'header': MODEL_INP_COLS['LINKS_CONDUITS']}) groups['links_weirs'].update({'header': MODEL_INP_COLS['LINKS_WEIRS']}) groups['links_orifices'].update({'header': MODEL_INP_COLS['LINKS_ORIFICES']}) groups['nodes_coordinates'].update({'header': MODEL_INP_COLS['NODES_COORDINATES']}) groups['nodes_outfalls'].update({'header': MODEL_INP_COLS['NODES_OUTFALLS']}) groups['nodes_storage'].update({'header': MODEL_INP_COLS['NODES_STORAGE']}) groups['nodes_junctions'].update({'header': MODEL_INP_COLS['NODES_JUNCTIONS']}) return groups # %% def list_files(directory, extension, prefix): return (f for f in listdir(directory) if (f.endswith('.' + extension)) & (f.startswith(prefix))) def raingages_meta_to_dfs(model, model_id): """ Read a .CSV into a Pandas DataFrame until a blank line is found, then stop. """ start = build_groups_dicts(model)['raingages']['start'] skip_rows = build_groups_dicts(model)['raingages']['line_to_skip'] header = ['raingage_id', 'format', 'interval', 'unit'] df = pd.DataFrame() with open(model, newline='') as f: contents = [] r = csv.reader(f) for i, line in enumerate(r): if i > start + 1: if i != skip_rows: if not line: break # elif i == start + 1: # headers = line else: formatted_line = [line[0].split()[0], line[0].split()[1], line[0].split()[2],line[0].split()[7]] contents.append(formatted_line) df = pd.DataFrame(data = contents, columns= header,) df['interval'] = df['interval'].map( lambda x: datetime.strptime(x, '%H:%M')) df.insert(0, 'precipitation_id', precipitation_id) print('raingages','df created!') return df def date_parser(line): year = line[0].split()[1] month = line[0].split()[2].zfill(2) day = line[0].split()[3].zfill(2) hour = line[0].split()[4].zfill(2) minute = line[0].split()[5].zfill(2) str_date = '-'.join([year, month, day, hour, minute] ) date_format = '%Y-%m-%d-%H-%M' return datetime.strptime(str_date, date_format) # %% def raingages_to_df(event_folder, event_id, model, model_id): contents = [] for file in list_files(event_folder, 'txt', 'P'): raingage_id = file.split('.')[0] with open(os.path.join(event_folder, file), newline='') as f: r = csv.reader(f) for i, line in enumerate(r): try: formatted_line = [ raingage_id, date_parser(line), line[0].split()[6] ] contents.append(formatted_line) except: print('error') df_timeseries = pd.DataFrame(data = contents, columns= ['raingage_id', 'elapsed_time', 'value']) df_timeseries.insert(0, 'precipitation_id', precipitation_id) df_metadata = raingages_meta_to_dfs(model, model_id) return df_metadata, df_timeseries # %% def load_raingages_to_db(event_folder, event_id, model, model_id): raingage_metadata, raingage_timeseries = raingages_to_df(event_folder, event_id, model, model_id) table_metadata = 'raingages_metadata' table_timeseries = 'raingages_timeseries' try: raingage_metadata.to_sql(table_metadata, engine_base_ina, index=False, if_exists='append') except Exception as e: print(e) try: raingage_timeseries.to_sql(table_timeseries, engine_base_ina, index=False, if_exists='append') except Exception as e: print(e) # def group_type_to_dfs(model, model_id, group, id_col, col_to_check, own_relevant__list, relevant_dependent_list): # """ Read a .CSV into a Pandas DataFrame until a blank line is found, then stop. # """ # start = build_groups_dicts(model)[group]['start'] # skip_rows = build_groups_dicts(model)[group]['line_to_skip'] # header = build_groups_dicts(model)[group]['header'] # global own_relevant__list # own_relevant_list = [] # df = pd.DataFrame() # with open(model, newline='') as f: # contents = [] # r = csv.reader(f) # for i, line in enumerate(r): # if i >= start + 1: # if i != skip_rows: # if not line: # break # # elif i == start + 1: # # headers = line # else: # if len(relevant_dependecy_list) == 0: # own_relevant__list.append(line[0].split()[id_col]) # contents.append(line[0].split()) # else: # if line[0].split()[col_to_check].lower() in relevant_dependent_list: # own_relevant__list.append(line[0].split()[id_col]) # contents.append(line[0].split()) # df = pd.DataFrame(data = contents, columns= [col.lower().replace("-", "_").replace("%", "").replace(" ", "_") for col in header],) # df.insert(0, 'model_id', model_id) # print(group,'df created!') # return df def conduits_to_dfs(model, model_id): """ Read a .CSV into a Pandas DataFrame until a blank line is found, then stop. """ start = build_groups_dicts(model)['links_conduits']['start'] skip_rows = build_groups_dicts(model)['links_conduits']['line_to_skip'] header = build_groups_dicts(model)['links_conduits']['header'] global RELEVANT_LINKS_CONDUITS RELEVANT_LINKS_CONDUITS = [] df = pd.DataFrame() with open(model, newline='') as f: contents = [] r = csv.reader(f) for i, line in enumerate(r): if i > start + 1: if i != skip_rows: if not line: break # elif i == start + 1: # headers = line else: if len(RELEVANT_LINKS) == 0: contents.append(line[0].split()) else: if line[0].split()[0].lower() in RELEVANT_LINKS: RELEVANT_LINKS_CONDUITS.append(line[0].split()[0]) contents.append(line[0].split()) df = pd.DataFrame(data = contents, columns= [col.lower().replace("-", "_").replace("%", "").replace(" ", "_") for col in header],) df.insert(0, 'model_id', model_id) print('conduits','df created!') return df def weirs_to_dfs(model, model_id): """ Read a .CSV into a Pandas DataFrame until a blank line is found, then stop. """ start = build_groups_dicts(model)['links_weirs']['start'] skip_rows = build_groups_dicts(model)['links_weirs']['line_to_skip'] header = build_groups_dicts(model)['links_weirs']['header'] global RELEVANT_LINKS_WEIRS RELEVANT_LINKS_WEIRS = [] df = pd.DataFrame() with open(model, newline='') as f: contents = [] r = csv.reader(f) for i, line in enumerate(r): if i > start + 1: if i != skip_rows: if not line: break # elif i == start + 1: # headers = line else: if len(RELEVANT_LINKS) == 0: contents.append(line[0].split()) else: if line[0].split()[0].lower() in RELEVANT_LINKS: RELEVANT_LINKS_WEIRS.append(line[0].split()[0]) contents.append(line[0].split()) df = pd.DataFrame(data = contents, columns= [col.lower().replace("-", "_").replace("%", "").replace(" ", "_") for col in header],) df.insert(0, 'model_id', model_id) print('weirs','df created!') return df def orifices_to_dfs(model, model_id): """ Read a .CSV into a Pandas DataFrame until a blank line is found, then stop. """ start = build_groups_dicts(model)['links_orifices']['start'] skip_rows = build_groups_dicts(model)['links_orifices']['line_to_skip'] header = build_groups_dicts(model)['links_orifices']['header'] global RELEVANT_LINKS_ORIFICES RELEVANT_LINKS_ORIFICES = [] df = pd.DataFrame() with open(model, newline='') as f: contents = [] r = csv.reader(f) for i, line in enumerate(r): if i > start + 1: if i != skip_rows: if not line: break # elif i == start + 1: # headers = line else: if len(RELEVANT_LINKS) == 0: contents.append(line[0].split()) else: if line[0].split()[0].lower() in RELEVANT_LINKS: RELEVANT_LINKS_ORIFICES.append(line[0].split()[0]) contents.append(line[0].split()) df = pd.DataFrame(data = contents, columns= [col.lower().replace("-", "_").replace("%", "").replace(" ", "_") for col in header],) df.insert(0, 'model_id', model_id) print('orifices','df created!') return df def get_nodes_from_links(model, model_id): conduits_df = conduits_to_dfs(model, model_id) orifices_df = orifices_to_dfs(model, model_id) weirs_df = weirs_to_dfs(model, model_id) links_dfs = [ conduits_df, orifices_df, weirs_df ] nodes = [] for df in links_dfs: for col in [col for col in df.columns if 'node' in col]: nodes += df[col].unique().tolist() return nodes #cambio de coordenadas def convert_coords(coord_tuple): transformer = pyproj.Transformer.from_crs(crs_from='epsg:' + epsg_modelo, crs_to='epsg:4326') lon, lat = transformer.transform(coord_tuple[0], coord_tuple[1]) return (lon,lat) def nodes_to_dfs(model, model_id): """ Read a .CSV into a Pandas DataFrame until a blank line is found, then stop. """ global RELEVANT_NODES RELEVANT_NODES = get_nodes_from_links(model, model_id) start = build_groups_dicts(model)['nodes_coordinates']['start'] skip_rows = build_groups_dicts(model)['nodes_coordinates']['line_to_skip'] header = build_groups_dicts(model)['nodes_coordinates']['header'] df = pd.DataFrame() with open(model, newline='') as f: contents = [] r = csv.reader(f) for i, line in enumerate(r): if i > start + 1: if i != skip_rows: if not line: break # elif (i == start + 1): # headers = line else: if len(RELEVANT_NODES) == 0: contents.append(line[0].split()) else: if line[0].split()[0] in RELEVANT_NODES: contents.append(line[0].split()) df = pd.DataFrame(data = contents, columns= [col.lower().replace("-", "_").replace("%", "").replace(" ", "_") for col in header],) df.insert(0, 'model_id', model_id) cols =['lat', 'lon'] coords = [] coordinates = [(j[0], j[1]) for i,j in df[['x_coord', 'y_coord']].iterrows()] pool = multiprocessing.Pool(8) coords.append(pool.map(convert_coords, coordinates)) pool.close() pool.join() # for i in df[['x_coord', 'y_coord']].iterrows(): # coords.append(convert_coords(i[1])) # from pyproj import Transformer # def convert_coords(coord_tuple): # global coords # transformer = Transformer.from_crs(crs_from='epsg:5348' , crs_to='epsg:4326') # lon, lat = transformer.transform(coord_tuple[0], coord_tuple[1]) # coords.append((lon, lat, coord_tuple[2])) # return coords # import concurrent.futures # coords = [] # with concurrent.futures.ProcessPoolExecutor(max_workers=8) as executor: # for result in executor.map(convert_coords, [(i[1], i[2], i[3]) for i in coordinates]): # pass # coords = result df = pd.concat([df, pd.DataFrame(coords[0], columns=cols)], axis=1) print('nodes','df created!') return df def outfalls_to_dfs(model, model_id): """ Read a .CSV into a Pandas DataFrame until a blank line is found, then stop. """ global RELEVANT_NODES RELEVANT_NODES = get_nodes_from_links(model, model_id) start = build_groups_dicts(model)['nodes_outfalls']['start'] skip_rows = build_groups_dicts(model)['nodes_outfalls']['line_to_skip'] header = build_groups_dicts(model)['nodes_outfalls']['header'] df = pd.DataFrame() with open(model, newline='') as f: contents = [] r = csv.reader(f) for i, line in enumerate(r): if i > start + 1: if i != skip_rows: if not line: break # elif (i == start + 1): # headers = line else: if len(RELEVANT_NODES) == 0: contents.append(line[0].split()) else: if line[0].split()[0] in RELEVANT_NODES: contents.append(line[0].split()) df = pd.DataFrame(data = contents, columns= [col.lower().replace("-", "_").replace("%", "").replace(" ", "_") for col in header],) df.insert(0, 'model_id', model_id) print('outfalls','df created!') return df def junctions_to_dfs(model, model_id): """ Read a .CSV into a Pandas DataFrame until a blank line is found, then stop. """ global RELEVANT_NODES RELEVANT_NODES = get_nodes_from_links(model, model_id) start = build_groups_dicts(model)['nodes_junctions']['start'] skip_rows = build_groups_dicts(model)['nodes_junctions']['line_to_skip'] header = build_groups_dicts(model)['nodes_junctions']['header'] df = pd.DataFrame() with open(model, newline='') as f: contents = [] r = csv.reader(f) for i, line in enumerate(r): if i > start + 1: if i != skip_rows: if not line: break # elif (i == start + 1): # headers = line else: if len(RELEVANT_NODES) == 0: contents.append(line[0].split()) else: if line[0].split()[0] in RELEVANT_NODES: contents.append(line[0].split()) df = pd.DataFrame(data = contents, columns= [col.lower().replace("-", "_").replace("%", "").replace(" ", "_") for col in header],) df.insert(0, 'model_id', model_id) print('junctions','df created!') return df def storage_to_dfs(model, model_id): """ Read a .CSV into a Pandas DataFrame until a blank line is found, then stop. """ global RELEVANT_NODES RELEVANT_NODES = get_nodes_from_links(model, model_id) start = build_groups_dicts(model)['nodes_storage']['start'] skip_rows = build_groups_dicts(model)['nodes_storage']['line_to_skip'] header = build_groups_dicts(model)['nodes_storage']['header'] df = pd.DataFrame() with open(model, newline='') as f: contents = [] r = csv.reader(f) for i, line in enumerate(r): if i > start + 1: if i != skip_rows: if not line: break # elif (i == start + 1): # headers = line else: if len(RELEVANT_NODES) == 0: contents.append(line[0].split()) else: if line[0].split()[0] in RELEVANT_NODES: contents.append(line[0].split()) df = pd.DataFrame(data = contents, columns= [col.lower().replace("-", "_").replace("%", "").replace(" ", "_") for col in header],) df.insert(0, 'model_id', model_id) print('storage','df created!') return df def subcatch_to_dfs(model, model_id): """ Read a .CSV into a Pandas DataFrame until a blank line is found, then stop. """ start = build_groups_dicts(model)['subcatchments']['start'] skip_rows = build_groups_dicts(model)['subcatchments']['line_to_skip'] header = build_groups_dicts(model)['subcatchments']['header'] global RELEVANT_SUBCATCHMENTS RELEVANT_SUBCATCHMENTS = [] df = pd.DataFrame() with open(model, newline='') as f: contents = [] r = csv.reader(f) for i, line in enumerate(r): if i > start + 1: if i != skip_rows: if not line: break # elif i == start + 1: # headers = line else: relevant_nodes = [node for node in RELEVANT_NODES] if len(relevant_nodes) == 0: contents.append(line[0].split()) else: if line[0].split()[2] in relevant_nodes: RELEVANT_SUBCATCHMENTS.append(line[0].split()[0]) contents.append(line[0].split()) df = pd.DataFrame(data = contents, columns= [col.lower().replace("-", "_").replace("%", "").replace(" ", "_") for col in header],) df.insert(0, 'model_id', model_id) print('subcatch','df created!') return df def infiltration_to_dfs(model, model_id): """ Read a .CSV into a Pandas DataFrame until a blank line is found, then stop. """ start = build_groups_dicts(model)['infiltration']['start'] skip_rows = build_groups_dicts(model)['infiltration']['line_to_skip'] header = build_groups_dicts(model)['infiltration']['header'] df = pd.DataFrame() with open(model, newline='') as f: contents = [] r = csv.reader(f) for i, line in enumerate(r): if i > start + 1: if i != skip_rows: if not line: break # elif i == start + 1: # headers = line else: relevant_nodes = [node for node in RELEVANT_NODES] if len(relevant_nodes) == 0: contents.append(line[0].split()) else: if line[0].split()[2] in relevant_nodes: contents.append(line[0].split()) df = pd.DataFrame(data = contents, columns= [col.lower().replace("-", "_").replace("%", "").replace(" ", "_") for col in header],) df.insert(0, 'model_id', model_id) print('infiltration','df created!') return df def subareas_to_dfs(model, model_id): """ Read a .CSV into a Pandas DataFrame until a blank line is found, then stop. """ start = build_groups_dicts(model)['subareas']['start'] skip_rows = build_groups_dicts(model)['subareas']['line_to_skip'] header = build_groups_dicts(model)['subareas']['header'] df = pd.DataFrame() with open(model, newline='') as f: contents = [] r = csv.reader(f) for i, line in enumerate(r): if i > start + 1: if i != skip_rows: if not line: break # elif i == start + 1: # headers = line else: relevant_nodes = [node for node in RELEVANT_NODES] if len(relevant_nodes) == 0: contents.append(line[0].split()) else: if line[0].split()[2] in relevant_nodes: contents.append(line[0].split()) df = pd.DataFrame(data = contents, columns= [col.lower().replace("-", "_").replace("%", "").replace(" ", "_") for col in header],) df.insert(0, 'model_id', model_id) print('subareas','df created!') return df # functions to interact with the db def df_to_db(df, engine, table, if_exists): if update_model: session_factory = sessionmaker(bind=engine_base_ina) Session = scoped_session(session_factory) df.to_sql(table, engine, index=False, if_exists=if_exists) Session.remove() print(elements,'to database!') def query_to_db(engine, query): session_factory = sessionmaker(bind=engine_base_ina) Session = scoped_session(session_factory) engine.execute(query) Session.remove() # def group_type_to_dfs(model, model_id, group, id_col, col_to_check, own_relevant__list, relevant_dependecy_list): # def inp_to_db(model, model_id, engine): # for gro # df_to_db( # group_type_to_dfs( # model, # model_id), # engine, # 'coordinates', # 'append' # ) # print('Listo coordinates!') def inp_to_db(model, model_id, engine): df_to_db( nodes_to_dfs( model, model_id), engine, 'nodes_coordinates', 'append' ) print('Listo coordinates!') for element in [col for col in elements if col != 'coordinates']: if element == 'links_conduits': df_to_db( conduits_to_dfs( model, model_id,), engine, element, 'append' ) print('Listo conduits!') if element == 'links_orifices': df_to_db( orifices_to_dfs( model, model_id,), engine, element, 'append' ) print('Listo orifices!') if element == 'links_weirs': df_to_db( weirs_to_dfs( model, model_id,), engine, element, 'append' ) print('Listo weirs!') if element == 'nodes_junctions': df_to_db( junctions_to_dfs( model, model_id,), engine, element, 'append' ) print('Listo junctions!') if element == 'nodes_storage': df_to_db( storage_to_dfs( model, model_id,), engine, element, 'append' ) print('Listo storages!') if element == 'nodes_outfalls': df_to_db( outfalls_to_dfs( model, model_id,), engine, element, 'append' ) print('Listo outfalls!') if element == 'subareas': df_to_db( subareas_to_dfs( model, model_id,), engine, element, 'append' ) print('Listo subareas!') if element == 'infiltration': df_to_db( infiltration_to_dfs( model, model_id,), engine, element, 'append' ) print('Listo infiltration!') if element == 'subcatchments': df_to_db( subcatch_to_dfs( model, model_id,), engine, element, 'append' ) print('Listo subcatch!') def time_series_vars_to_db_multiproc(model_out, tipo, evento, conn, sub_set, cols_tipo): """ Esta función genera un diccionario con todas las series temporales del modelo para una tipo. El diccionario tiene la siguiente estructura: {tipo : {parte: {variable: serie} } } Argumentos: model.out: el archivo .out que devuelve swmm tipo: str --> The type are "subcatchment", "node", "link", "pollutant", "system". evento: ID del evento Return: dicc # hace un insert en la base de datos """ series = {tipo: {}} partes = set([item[1] for item in swmm.catalog(model_out) if item[0] == tipo]) if len(sub_set) == 0: partes = [parte for parte in partes] else: partes = [parte for parte in partes if parte in sub_set] print('Cantidad de partes:', len(partes), partes) count = 0 global time_series_vars_to_db # for parte in partes: def time_series_vars_to_db(parte): nonlocal count series_df = swmm.extract(model_out, tipo + ',' + parte + ',') series_df.columns = [col[len(tipo + '_' + parte + '_'):].lower() for col in series_df.columns] series_df.reset_index(inplace=True) series_df = series_df.rename({'index':'elapsed_time'}, axis=1) series_df[tipo + '_id'] = parte series_df['event_id'] = evento series_df = series_df[cols_tipo] series.get(tipo).update({parte: series_df}) print(tipo, parte) tabla = 'events_' + tipo + 's' # session_factory = sessionmaker(bind=engine_base_ina) # Session = scoped_session(session_factory) engine_base_ina.dispose() series.get(tipo).get(parte).to_sql(tabla, conn, index=False, if_exists='append') # Session.remove() count += 1 print(tipo + ': ' + str(count) + ' de ' + str(len(partes))) return series pool = multiprocessing.Pool(10) pool.map(time_series_vars_to_db, partes) pool.close() pool.join() def out_to_db(model_out, event, engine): for tipo in RELEVANT_GROUP_TYPES_OUT: if tipo == 'link': time_series_vars_to_db_multiproc(model_out, tipo, event, engine, RELEVANT_LINKS, MODEL_OUT_COLS['LINKS_COLS']) elif tipo == 'node': time_series_vars_to_db_multiproc(model_out, tipo, event, engine, RELEVANT_NODES, MODEL_OUT_COLS['NODES_COLS']) elif tipo == 'subcatchment': time_series_vars_to_db_multiproc(model_out, tipo, event, engine, RELEVANT_SUBCATCHMENTS, MODEL_OUT_COLS['SUBCATCHMENTS_COLS']) if __name__ == "__main__": # The Session object created here will be used by the function. starttime = time.time() update_raingage = True update_model = True update_event = True try: query_to_db(engine_base_ina, "INSERT INTO precipitation_event(precipitation_id) VALUES('{}')".format(precipitation_id)) except Exception as e: # update_model = False print('precipitation already loaded!') load_raingages_to_db(event_folder, event_id, model_inp, model_id) try: query_to_db(engine_base_ina, "INSERT INTO models(model_id) VALUES('{}')".format(model_id)) except Exception as e: # update_model = False print('Model already loaded!') try: query_to_db(engine_base_ina, "INSERT INTO events(event_id, model_id, precipitation_id) VALUES('{}', '{}', '{}')".format(event_id, model_id, precipitation_id)) except Exception as e: # update_event = False print('Event, model and raingage already loaded!') groups = {} elements = group_start_line(model_inp).keys() inp_to_db(model_inp, model_id, engine_base_ina) out_to_db(model_out, event_id, engine_base_ina) print('Listo todo!') print('That took {} seconds'.format(time.time() - starttime))
# -*- coding: utf-8 -*- ############################################################################# # @package ad_hmi.framework # @brief init methode of python package ad_hmi.framework. ############################################################################# # @author <NAME> # @copyright (c) All rights reserved. ############################################################################# import numpy as np from .LaneMark import LaneMark from .Line import Line from .NavArrow import NavArrow class Road: ( LANE_LEFT, LANE_EGO, LANE_RIGHT, ) = range(3) def __init__(self, count=LaneMark.IDX_NUM): self._lane_marks = [] self._nav_arrow = NavArrow(self) self.init_lane_marks(count) def reset(self): pass # TODO: def init_lane_marks(self, count=0): self._lane_marks = [] for n in range(count): self._lane_marks.append(LaneMark([0, 0, 0])) def high_light_ego_lane(self, active=False): if active: self._lane_marks[LaneMark.IDX_L]._type = LaneMark.HIGH_LIGHT self._lane_marks[LaneMark.IDX_R]._type = LaneMark.HIGH_LIGHT def update_ego_lane(self): if self._lane_marks[LaneMark.IDX_L]._state == LaneMark.ST_AVAILABLE \ and self._lane_marks[LaneMark.IDX_R]._state == LaneMark.ST_AVAILABLE: lane = self._lane_marks[LaneMark.IDX_EGO] lane.copy(self._lane_marks[LaneMark.IDX_L]) lane.c0 = (self._lane_marks[LaneMark.IDX_L].c0 + self._lane_marks[LaneMark.IDX_R].c0)/2 lane._state = LaneMark.ST_AVAILABLE lane._type = LaneMark.EGO_LANE lane._width = abs(self._lane_marks[LaneMark.IDX_L].c0 - self._lane_marks[LaneMark.IDX_R].c0) - LaneMark.LANE_MARK_WIDTH else: self._lane_marks[LaneMark.IDX_EGO]._state = LaneMark.ST_INVALID def update_ldw_wrn(self, warning=False): if warning: if abs(self._lane_marks[LaneMark.IDX_L].c0) <= abs( self._lane_marks[LaneMark.IDX_R].c0): self._lane_marks[LaneMark.IDX_L]._type = LaneMark.LDW_WRN else: self._lane_marks[LaneMark.IDX_R]._type = LaneMark.LDW_WRN def calc_ego_lane_center_polyline(self): ''' used for lane painting ''' # no lane mark if len(self._lane_marks) < 2: return [] ego_left = self._lane_marks[0] ego_right = self._lane_marks[1] if ego_left._state != LaneMark.ST_AVAILABLE or ego_right._state != LaneMark.ST_AVAILABLE: return [] dx_start = min(ego_left.dx_start, ego_right.dx_start) dx_end = min(ego_left.dx_end, ego_right.dx_end) delta = min(ego_left._delta, ego_right._delta) x = np.arange(dx_start, dx_end, delta) y_left = Line.Y5(x, ego_left.c0, ego_left.c1, ego_left.c2, ego_left.c3, ego_left.c4, ego_left.c5) y_right = Line.Y5(x, ego_right.c0, ego_right.c1, ego_right.c2, ego_right.c3, ego_right.c4, ego_right.c5) y = (y_left + y_right) / 2 z = np.zeros(x.shape) xyz = np.array([x, y, z]).T.flatten() # print("xyz=", xyz) return xyz def get_ego_lane_style(self, is_active=False): if is_active: return {'stroke_width': 3.0, 'stroke_color': [0xb8, 0xf4, 0xd6, 0x88]} # normal return {'stroke_width': 3.0, 'stroke_color': [0xEE, 0xEE, 0xEE, 0x80]} def get_lane_width(self, idx): if idx == Road.LANE_LEFT: return abs(self._lane_marks[LaneMark.IDX_LL].c0 - self._lane_marks[LaneMark.IDX_L].c0) elif idx == Road.LANE_EGO: return abs(self._lane_marks[LaneMark.IDX_L].c0 - self._lane_marks[LaneMark.IDX_R].c0) elif idx == Road.LANE_RIGHT: return abs(self._lane_marks[LaneMark.IDX_R].c0 - self._lane_marks[LaneMark.IDX_RR].c0)
"""Vanquisher base terrain code. This module is concerned with the representation and generation of specifically terrain. Every world.Chunk has a terrain property of type TerrainChunk. """ import math import typing from ...numba import maybe_numba_jit if typing.TYPE_CHECKING: from . import generator try: from ._interpolate import ffi from ._interpolate.lib import bilinear USE_CFFI_INTERPOLATOR = True except ImportError: USE_CFFI_INTERPOLATOR = False class TerrainChunk: """A square chunk of terrain. This is the terrain part of the game's concept of chunk. You can tell because Chunk.terrain is always a TerrainChunk. """ def __init__(self, width=32): """TerrainChunk initializer. Creates a new terrain, with the given square width and configuration, and with a heightmap that defaults to a flat plane with every point at height zero. You can then use a TerrainGenerator to make this terrain more interesting. In general, though, let World handle this job, unless you really want to use TerrainChunk directly and manually. """ self.heightmap = ffi.new("float[]", width * width) self.width = width def get(self, x_pos: int, y_pos: int) -> float: """A terrain height getter, at aligned (integer) positions, uninterpolated. Gets the height at the specified integer position of the heightmap. Use an indexing syntax instead unless you know what you are doing. """ return self.heightmap[y_pos * self.width + x_pos] @staticmethod @maybe_numba_jit(nopython=True) def _bilinear_interpolate( val_a: float, val_b: float, val_c: float, val_d: float, x_pos: float, y_pos: float, x_lo: float, x_hi: float, y_lo: float, y_hi: float, ) -> float: """Bilinear interpolation without fuss. Made for Numba.""" weight_a = (x_hi - x_pos) * (y_hi - y_pos) weight_b = (x_hi - x_pos) * (y_pos - y_lo) weight_c = (x_pos - x_lo) * (y_hi - y_pos) weight_d = (x_pos - x_lo) * (y_pos - y_lo) return weight_a * val_a + weight_b * val_b + weight_c * val_c + weight_d * val_d def __getitem__(self, coords: typing.Tuple[float, float]) -> float: """A terrain height getter. Gets the height at any point of this TerrainChunk, including using bilinear interpolation. Uses the Cython interpolator whenever possible. """ if USE_CFFI_INTERPOLATOR: res = bilinear(self.width, *coords, self.heightmap) if math.isnan(res): raise ValueError( ("Got NaN trying to interpolate position ({0[0]},{0[1]})").format( coords ) ) return res (x_pos, y_pos) = coords if x_pos < 0.0: x_pos = 0.0 if x_pos > self.width - 1.0: x_pos = self.width - 1.0001 # tiny epsilon for flooring purposes if y_pos < 0.0: y_pos = 0.0 if y_pos > self.width - 1.0: y_pos = self.width - 1.0001 # tiny epsilon for flooring purposes x_lo = math.floor(x_pos) x_hi = x_lo + 1 y_lo = math.floor(y_pos) y_hi = y_lo + 1 val_a = self.get(x_lo, y_lo) val_b = self.get(x_lo, y_hi) val_c = self.get(x_hi, y_lo) val_d = self.get(x_hi, y_hi) return self._bilinear_interpolate( val_a, val_b, val_c, val_d, x_pos, y_pos, x_lo, x_hi, y_lo, y_hi ) def __setitem__(self, pos: typing.Tuple[int, int], value: float): """Sets a value of this TerrainChunk heightmap.""" (x_pos, y_pos) = pos self.heightmap[y_pos * self.width + x_pos] = value @maybe_numba_jit(nopython=False) def generate( self, generator: "generator.TerrainGenerator", offset: typing.Tuple[int, int] = (0, 0), ): """Generate the heightmap according to the passed TerrainGenerator. Generates the heightmap of this terrain chunk from a TerrainGenerator instance. """ x_offset, y_offset = offset for i_pos in range(self.width * self.width): x_pos = i_pos % self.width y_pos = math.floor(i_pos / self.width) self[x_pos, y_pos] = generator.height_at(x_pos + x_offset, y_pos + y_offset)
"""Stats command""" import traceback import json import math import botutils from library import fancy from botc import Phase, RoleGuide from discord.ext import commands with open('botutils/bot_text.json') as json_file: language = json.load(json_file) error_str = language["system"]["error"] with open('botc/game_text.json') as json_file: documentation = json.load(json_file) current_phase = documentation["gameplay"]["current_phase"] stats_tied = documentation["gameplay"]["stats_tied"] stats_no_one = documentation["gameplay"]["stats_no_one"] stats_chopping = documentation["gameplay"]["stats_chopping"] stats_header = documentation["gameplay"]["stats_header"] votes_stats = documentation["gameplay"]["votes_stats"] stats_1 = documentation["gameplay"]["stats_1"] stats_2 = documentation["gameplay"]["stats_2"] stats_3 = documentation["gameplay"]["stats_3"] setup_info = documentation["gameplay"]["setup_info"] class Stats(commands.Cog, name = documentation["misc"]["townhall_cog"]): """BoTC in-game commands cog Stats command - used for viewing the game's player statistics """ def __init__(self, client): self.client = client def cog_check(self, ctx): """Check the channel of the context, return True if it is sent in either lobby, or in spec chat. Admins can bypass. """ return botutils.check_if_admin(ctx) or \ botutils.check_if_lobby(ctx) or \ botutils.check_if_dm(ctx) or \ botutils.check_if_spec(ctx) # ---------- STATS COMMAND (Stats) ---------------------------------------- @commands.command( pass_context = True, name = "stats", hidden = False, brief = documentation["doc"]["stats"]["brief"], help = documentation["doc"]["stats"]["help"], description = documentation["doc"]["stats"]["description"] ) async def stats(self, ctx): """Stats command usage: stats can be used by all players, spectators in spec-chat or in DM """ import globvars game = globvars.master_state.game nb_total_players = len(game.sitting_order) # Header information - edition, phase and game title msg = ctx.author.mention msg += " " msg += stats_header.format(game.nb_players, fancy.bold(game.gamemode.value)) msg += "\n" msg += current_phase.format(fancy.bold(game.current_phase.value)) msg += "\n" # Setup information role_guide = RoleGuide(nb_total_players) nb_townsfolks = role_guide.nb_townsfolks nb_outsiders = role_guide.nb_outsiders nb_minions = role_guide.nb_minions nb_demons = role_guide.nb_demons msg += setup_info.format( nb_total_players, nb_townsfolks, "s" if nb_townsfolks > 1 else "", nb_outsiders, "s" if nb_outsiders > 1 else "", nb_minions, "s" if nb_minions > 1 else "", nb_demons, "s" if nb_demons > 1 else "" ) msg += "\n" nb_alive_players = len([player for player in game.sitting_order if player.is_apparently_alive()]) nb_available_votes = len([player for player in game.sitting_order if player.has_vote()]) # Vote stats msg += votes_stats.format( total = nb_total_players, emoji_total = botutils.BotEmoji.people, alive = nb_alive_players, emoji_alive = botutils.BotEmoji.alive, votes = nb_available_votes, emoji_votes = botutils.BotEmoji.votes ) # If the phase is daytime, then include voting information if game.current_phase == Phase.day: chopping_block = game.chopping_block msg += "\n" if chopping_block: player_about_to_die = chopping_block.player_about_to_die nb_votes = chopping_block.nb_votes if player_about_to_die: msg += stats_chopping.format(player_about_to_die.game_nametag, nb_votes) msg += " " msg += stats_1.format(nb_votes, nb_votes + 1) else: msg += stats_tied.format(nb_votes) msg += " " msg += stats_2.format(nb_votes + 1) else: msg += stats_no_one msg += " " msg += stats_3.format(math.ceil(nb_alive_players / 2)) msg += game.create_sitting_order_stats_string() await ctx.send(msg) @stats.error async def stats_error(self, ctx, error): # Check did not pass -> commands.CheckFailure if isinstance(error, commands.CheckFailure): return else: try: raise error except Exception: await ctx.send(error_str) await botutils.log(botutils.Level.error, traceback.format_exc()) def setup(client): client.add_cog(Stats(client))
<reponame>CoderAariz/Shop-Management-System<gh_stars>1-10 from tkinter import * from tkinter import messagebox import mysql.connector try: con=mysql.connector.connect(user='root',password='<PASSWORD>',database='shop_management',host='localhost') cur=con.cursor() def register(): global register_screen register_screen=Toplevel(top,cursor='arrow') register_screen.title('New Registration') register_screen.geometry('600x600') register_screen.configure(background='light blue') Label(register_screen, text="Please enter the details below", fg="green",font='Times 20 bold',background='light blue').pack() Label(register_screen, text="",background='light blue').pack() reg_name=Label(register_screen,text='UserName:',background='light blue',font='Calibiri 14 bold') reg_name.pack() global reg_name_ent reg_name_ent=Entry(register_screen) reg_name_ent.pack() reg_pas=Label(register_screen,text='Password:',background='light blue',font='Calibiri 14 bold') reg_pas.pack() global reg_pas_ent reg_pas_ent=Entry(register_screen,show='*') reg_pas_ent.pack() Label(register_screen, text="",background='light blue').pack() regbtn=Button(register_screen,text='Register',command=registration,bg='yellow',width=10,height=1,padx=3,pady=3) regbtn.pack() register_screen.mainloop() def registration(): global new_user new_user=reg_name_ent.get() global new_password new_password=reg_pas_ent.get() register_screen.destroy() user_info=(new_user,new_password) add_user=("INSERT INTO login (username,password) VALUES (%s, %s)") cur.execute(add_user,user_info) con.commit() messagebox.showinfo('Registration','Registration successful.',) initial() def login(): try: loaduser=("select * from login where username= %s") user=(name_ent.get(),) password=<PASSWORD>() cur.execute(loaduser,user) usercheck=cur.fetchall() u=usercheck[0][0] p=usercheck[0][1] if user[0]==u and password==p: initial() else: messagebox.showerror('Error','Username or Password is incorrect.Please try again.') except: messagebox.showerror('Error','There is an error in connection.') def initial(): global initial_screen initial_screen=Toplevel(top) initial_screen.configure(background='light blue') initial_screen.title('Welcome') initial_screen.geometry('600x600') Label(initial_screen,text='Welcome to Shop Management System',font='Times 20 bold ',background='light blue',foreground='black').pack() Label(initial_screen,text="",background='light blue').pack() Label(initial_screen,text="",background='light blue').pack() Label(initial_screen,text="",background='light blue').pack() search_btn=Button(initial_screen,text='Search',height=2,width=10,bg='yellow',command=search,font='Calibiri 14 bold') search_btn.pack() Label(initial_screen,text="",background='light blue').pack() Label(initial_screen,text="",background='light blue').pack() bill_btn=Button(initial_screen,text='Billing',height=2,width=10,bg='yellow',command=bill,font='Calibiri 14 bold') bill_btn.pack() Label(initial_screen,text="",background='light blue').pack() Label(initial_screen,text="",background='light blue').pack() exit_btn=Button(initial_screen,text='Kill Switch',height=2,width=10,bg='yellow',command=exit,font='Calibiri 14 bold') exit_btn.pack() initial_screen.mainloop() def bill(): initial_screen.destroy() global bill_screen bill_screen=Toplevel(top) bill_screen.title('Bill Generation') bill_screen.geometry('600x600') bill_screen.configure(background='light blue') cst_name=Label(bill_screen,text='Customer Name:',background='light blue') cst_name.grid(row=1,column=0) cst_name_ent=Entry(bill_screen) cst_name_ent.grid(row=1,column=1) cst_add=Label(bill_screen,text='Address:',background='light blue') cst_add.grid(row=3,column=0) cst_add_ent=Entry(bill_screen) cst_add_ent.grid(row=3,column=1) cst_mob=Label(bill_screen,text='Mobile no.:',background='light blue') cst_mob.grid(row=5,column=0) cst_mob_ent=Entry(bill_screen) cst_mob_ent.grid(row=5,column=1) Label(bill_screen,text='Product Name:',background='light blue').grid(row=7,column=0) p1=Entry(bill_screen).grid(row=7,column=1) Label(bill_screen,text='Price:',background='light blue').grid(row=7,column=2) p11=Entry(bill_screen).grid(row=7,column=3) Label(bill_screen,text='Product Name:',background='light blue').grid(row=9,column=0) p2=Entry(bill_screen).grid(row=9,column=1) Label(bill_screen,text='Price:',background='light blue').grid(row=9,column=2) p22=Entry(bill_screen).grid(row=9,column=3) Label(bill_screen,text='Product Name:',background='light blue').grid(row=11,column=0) p3=Entry(bill_screen).grid(row=11,column=1) Label(bill_screen,text='Price:',background='light blue').grid(row=11,column=2) p33=Entry(bill_screen).grid(row=11,column=3) Label(bill_screen,text='Product Name:',background='light blue').grid(row=13,column=0) p4=Entry(bill_screen).grid(row=13,column=1) Label(bill_screen,text='Price:',background='light blue').grid(row=13,column=2) p44=Entry(bill_screen).grid(row=13,column=3) Label(bill_screen,text='Product Name:',background='light blue').grid(row=15,column=0) p5=Entry(bill_screen).grid(row=15,column=1) Label(bill_screen,text='Price:',background='light blue').grid(row=15,column=2) p55=Entry(bill_screen).grid(row=15,column=3) print=Button(bill_screen,text='Print Bill',bg='yellow',width=10,height=1,padx=3,pady=3).grid(row=25,column=2) bill_screen.mainloop() def search(): initial_screen.destroy() global search search=Toplevel(top) search.geometry('800x600') search.configure(background='light blue') Label(search,background='light blue',text='Enter the product name to search the product',font='Times 24 bold').pack() Label(search,text=' ',background='light blue').pack() Label(search,text=' ',background='light blue').pack() product=Label(search,text='Product Name',background='light blue',font='Times 20 bold') product.pack() Label(search,text=' ',background='light blue').pack() Label(search,text=' ',background='light blue').pack() global product_e product_e=Entry(search) product_e.pack() Label(search,text=' ',background='light blue').pack() Label(search,text=' ',background='light blue').pack() search_btn=Button(search,text='Search',height=1,width=9,bg='yellow',font='calibiri 10 bold',command=prdct_search) search_btn.pack() search.mainloop() def prdct_search(): try: global search_screen search_screen=Toplevel(top) search_screen.geometry('600x600') search_screen.configure(background='light blue') prd=(product_e.get(),) search.destroy() Label(search_screen,background='light blue',text='Product details',font='Times 20 bold').place(x=40,y=20) searchproduct=("select * from product where name=%s") cur.execute(searchproduct,prd) prdct=cur.fetchall() Label(search_screen,text='Product',background='light blue').grid(row=0,column=0) Label(search_screen,text='Price',background='light blue').grid(row=0,column=2) Label(search_screen,text=prdct[0][0],background='light blue').grid(row=2,column=0) Label(search_screen,text=prdct[0][1],background='light blue').grid(row=2,column=2) search_screen.mainloop() except: messagebox.showerror('Error','searching failed.') global top top=Tk(screenName='Aariz',baseName='shopping',className=' ',useTk=1) top.geometry('600x600') top.configure(background='light blue') top.title('Shop Management System') Label(top,text='Login to continue..',background='light blue',foreground='black',font='Georgia 18 bold underline').pack() Label(text=' ',background='light blue').pack() Label(text=' ',background='light blue').pack() name_l=Label(top,text='UserName:',background='light blue',font='Calibiri 14 bold') name_l.pack() global name_ent name_ent=Entry(top) name_ent.pack() Label(text=' ',background='light blue').pack() pas_l=Label(top,text='Password:',background='light blue',font='Calibiri 14 bold',foreground='black') pas_l.pack() global pas_ent pas_ent=Entry(top,show='*') pas_ent.pack() Label(text=' ',background='light blue').pack() log_btn=Button(top,text='Login',activebackground='blue',activeforeground='green',bg='yellow',width=16,height=1,padx=3,pady=3,command=login,font='Calibiri 9 bold') log_btn.pack() Label(text=' ',background='light blue').pack() reg_l=Label(top,text='New user? Register here.',background='light blue',font='Calibiri 14 bold') reg_l.pack() reg_btn=Button(top,text='Register',height=1,width=16,bg='yellow',activebackground='blue',activeforeground='green',padx=3,pady=3,command=register,font='Calibiri 9 bold') reg_btn.pack() top.mainloop() except: messagebox.showerror('Error','Sorry an error occured in connection.') finally: cur.close() con.close()
<gh_stars>1-10 ''' Precondition successfully pass a users test. ''' from datetime import datetime, timedelta import time import pytest import requests from kii import AccountType, exceptions as exc, results as rs from kii.data import BucketType, clauses as cl from tests.conf import ( get_env, get_api_with_test_user, cleanup, ) GROUP_NAME = 'test_group' BUCKET_ID = 'test_bucket' class TestIteration: def setup_method(self, method): """ setup any state tied to the execution of the given method in a class. setup_method is invoked for every test method of a class. """ cleanup() self.api = get_api_with_test_user() self.scope = self.api.data.application self.bucket = self.scope(BUCKET_ID) self.OBJ_COUNT = 10 self._create_test_objects() def teardown_method(self, method): """ teardown any state that was previously setup with a setup_method call. """ try: self.scope.delete_a_bucket(BUCKET_ID) except exc.KiiBucketNotFoundError: pass cleanup() def _create_test_objects(self): for i in range(self.OBJ_COUNT): even = i % 2 == 0 self.bucket.create_an_object({ 'index': i, 'desc': 'An object number is {0}.'.format(i + 1), 'name': 'test user', 'even': even, }) def test_query_all_objects(self): # all results = self.bucket.query().best_effort_limit(50).all() assert len(results) == self.OBJ_COUNT def test_best_effort_limit_greater_than_limit(self): # best effort limit LIMIT = 1 BEST_EFFORT_LIMIT = 5 results = self.bucket.query() \ .best_effort_limit(BEST_EFFORT_LIMIT) \ .limit(LIMIT).order_by('index', False).all() assert len(results) == LIMIT assert results[0]['index'] == 0 def test_best_effort_limit_less_than_limit(self): LIMIT = 5 BEST_EFFORT_LIMIT = 1 results = self.bucket.query() \ .best_effort_limit(BEST_EFFORT_LIMIT) \ .limit(LIMIT).order_by('index', False).all() assert len(results) == LIMIT def test_best_effort_limit(self): BEST_EFFORT_LIMIT = 2 results = self.bucket.query() \ .best_effort_limit(BEST_EFFORT_LIMIT) \ .order_by('index', False).all() assert len(results) == self.OBJ_COUNT def test_limit(self): LIMIT = 2 results = self.bucket.query() \ .limit(LIMIT) \ .order_by('index', False).all() assert len(results) == LIMIT def test_slice(self): results = self.bucket.query().all() assert len(results[:5]) == 5 for i, r in enumerate(results[:5]): assert r['index'] == i assert len(results[3:]) == self.OBJ_COUNT - 3 for i, r in enumerate(results[3:]): assert r['index'] == 3 + i def test_offset(self): OFFSET = 2 results = self.bucket.query() \ .offset(OFFSET) \ .order_by('index', False).all() assert len(results) == self.OBJ_COUNT - OFFSET assert results[0]['index'] == OFFSET def test_offset_and_limit(self): OFFSET = 3 LIMIT = 3 results = self.bucket.query() \ .offset(OFFSET) \ .limit(LIMIT) \ .order_by('index', False).all() assert len(results) == LIMIT for i, r in enumerate(results): assert r['index'] == i + OFFSET def test_offset_and_limit_and_best_effort_limit(self): OFFSET = 1 LIMIT = 6 BEST_EFFORT_LIMIT = 2 results = self.bucket.query() \ .offset(OFFSET) \ .limit(LIMIT) \ .best_effort_limit(BEST_EFFORT_LIMIT) \ .order_by('index', False).all() assert len(results) == LIMIT for i, r in enumerate(results): assert r['index'] == i + OFFSET def test_pop(self): results = self.bucket.query() \ .order_by('index', False).all() assert len(results) == self.OBJ_COUNT last = results.pop() assert last assert last['index'] == self.OBJ_COUNT - 1 assert len(results) == self.OBJ_COUNT - 1 for i, r in enumerate(results): assert r['index'] == i head = results.pop(0) assert head['index'] == 0 assert len(results) == self.OBJ_COUNT - 2 for i, r in enumerate(results): assert r['index'] == i + 1 def test_pop_and_limit(self): LIMIT = 7 results = self.bucket.query() \ .limit(LIMIT) \ .order_by('index', False).all() assert len(results) == LIMIT last = results.pop() assert last assert last['index'] == LIMIT - 1 assert len(results) == LIMIT - 1 for i, r in enumerate(results): assert r['index'] == i head = results.pop(0) assert head['index'] == 0 assert len(results) == LIMIT - 2 for i, r in enumerate(results): assert r['index'] == i + 1 def test_pop_and_best_effort_limit(self): BEST_EFFORT_LIMIT = 2 results = self.bucket.query() \ .best_effort_limit(BEST_EFFORT_LIMIT) \ .order_by('index', False).all() assert len(results) == self.OBJ_COUNT last = results.pop() assert last assert last['index'] == self.OBJ_COUNT - 1 assert len(results) == self.OBJ_COUNT - 1 for i, r in enumerate(results): assert r['index'] == i head = results.pop(0) assert head['index'] == 0 assert len(results) == self.OBJ_COUNT - 2 for i, r in enumerate(results): assert r['index'] == i + 1 def test_pop_and_offset(self): OFFSET = 2 results = self.bucket.query() \ .offset(OFFSET) \ .order_by('index', False).all() assert len(results) == self.OBJ_COUNT - OFFSET last = results.pop() assert last assert last['index'] == self.OBJ_COUNT - 1 assert len(results) == self.OBJ_COUNT - OFFSET - 1 for i, r in enumerate(results): assert r['index'] == i + OFFSET head = results.pop(0) assert head['index'] == OFFSET assert len(results) == self.OBJ_COUNT - OFFSET - 2 for i, r in enumerate(results): assert r['index'] == i + 1 + OFFSET def test_pop_and_limit_and_best_effort_limit_and_offset(self): OFFSET = 2 BEST_EFFORT_LIMIT = 2 LIMIT = 4 results = self.bucket.query() \ .limit(LIMIT) \ .best_effort_limit(BEST_EFFORT_LIMIT) \ .offset(OFFSET) \ .order_by('index', False).all() assert len(results) == LIMIT last = results.pop() assert last assert last['index'] == OFFSET + LIMIT - 1 assert len(results) == LIMIT - 1 for i, r in enumerate(results): assert r['index'] == i + OFFSET head = results.pop(0) assert head['index'] == OFFSET assert len(results) == LIMIT - 2 for i, r in enumerate(results): assert r['index'] == i + 1 + OFFSET def test_pop_zero(self): results = self.bucket.query() \ .order_by('index', False).all() assert len(results) == self.OBJ_COUNT counter = 0 while results: counter += 1 last = results.pop() if not last: break assert last assert last['index'] == self.OBJ_COUNT - counter if last['index'] == 0: assert bool(results) is False else: assert bool(results) is True assert counter == self.OBJ_COUNT
<reponame>cfmcdonald-78/Hexcrawler ''' Created on Jul 25, 2012 @author: Chris ''' import core.event_manager as event_manager import core.options as options import pygame.mixer import os, random import gamemap.mask as mask import gui.component as component sound_events = [event_manager.UNIT_BLOCK, event_manager.UNIT_HEAL, event_manager.UNIT_HIT, event_manager.RANGED_ATTACK, event_manager.MOVE_DONE, event_manager.TURN_START, event_manager.COMBAT_START, event_manager.COMBAT_END, event_manager.BUTTON_CLICK, event_manager.MUSIC_CHANGE] combat_sound_events = [event_manager.UNIT_BLOCK, event_manager.UNIT_HEAL, event_manager.UNIT_HIT, event_manager.RANGED_ATTACK] sound_file_table = {event_manager.UNIT_BLOCK: "block.wav", event_manager.UNIT_HEAL: "angel.wav", event_manager.UNIT_HIT: "grunt.wav", event_manager.MOVE_DONE: "march.wav", event_manager.RANGED_ATTACK: "twang.wav", event_manager.TURN_START: "turn.wav", event_manager.BUTTON_CLICK: "click.wav"} TITLE_MUSIC = "Title music" BACKGROUND_MUSIC = "Background music" title_music = ["greensleeves-francis.mp3"] background_music = ["crooked-corsair-inn.mp3", "into-the-abyss.mp3", "land-of-fantasy.mp3"] #"PraetoriusBransle.ogg"] music_tracks = {TITLE_MUSIC: title_music, BACKGROUND_MUSIC: background_music} class SoundManager(object): def __init__(self): self.mask = None self.active_combat = False self.sound_active = True # self.paused_music = None pygame.mixer.init() self.sound_table = {} for sound_event in sound_file_table: self.sound_table[sound_event] = self.prep_sound_file(sound_file_table[sound_event]) def initialize(self): event_manager.add_listener_multi(self.handle_event, sound_events) pygame.mixer.music.set_endevent(component.MUSIC_DONE) # def do_battle_music(self): # if options.curr_options.music: # pygame.mixer.music.load(os.path.join('data', 'music', "kings-valor.mp3")) # pygame.mixer.music.set_volume(options.curr_options.music_volume) # pygame.mixer.music.play() def do_soundtrack(self, mode): if options.curr_options.music: music_file_name = random.choice(music_tracks[mode]) pygame.mixer.music.load(os.path.join('data', 'music', music_file_name)) pygame.mixer.music.set_volume(options.curr_options.music_volume) pygame.mixer.music.play() else: self.stop_soundtrack() # def stop_soundtrack(self): pygame.mixer.music.stop() def handle_event(self, event): if event.type == event_manager.MUSIC_CHANGE: options.curr_options.music = event.data['on'] self.do_soundtrack(BACKGROUND_MUSIC) return # # if event.data['on']: # self.start_soundtrack() # else: # self.stop_soundtrack() # return play_sound = options.curr_options.sound if event.type == event_manager.COMBAT_START: loc = event.data['hex_loc'] if self.mask != None and self.mask.get_visibility(loc.x, loc.y) == mask.VISIBLE: # print "visible combat!" self.active_combat = True # self.do_battle_music() play_sound = False elif event.type == event_manager.COMBAT_END: # if self.active_combat: # self.do_soundtrack(BACKGROUND_MUSIC) self.active_combat = False play_sound = False elif event.type == event_manager.MOVE_DONE: end_loc = event.data['hex_loc'] start_loc = event.data['hex_start'] if end_loc == start_loc or self.mask == None or self.mask.get_visibility(end_loc.x, end_loc.y) != mask.VISIBLE: play_sound = False elif event.type == event_manager.TURN_START: if event.data['player'] != self.mask.get_player(): play_sound = False elif event.type in combat_sound_events: # play only if there's an active combat visible to player play_sound = play_sound and self.active_combat if play_sound: self.sound_table[event.type].play() def set_mask(self, new_mask): self.mask = new_mask def prep_sound_file(self, file_name): return pygame.mixer.Sound(os.path.join('data', 'sound', file_name))
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ @author: <NAME> """ from string import ascii_lowercase from random import shuffle, choices from array import array import numpy as np from ETC.seq.check import zeroes def cast(seq): if seq is not None: if isinstance(seq, np.ndarray): try: out = seq.astype("uint32") if zeroes(out): print("> Input contains 0!") print("> Symbols shifted up by 1 ") return out + 1 return out except TypeError as error: print("ERROR:", error) print("> Input must be a list/tuple/array of positive integers!") print('> Recode or partition using "ETC.seq.recode"') return None except OverflowError as error: print("ERROR:", error) print("> Input must be a list/tuple/array of positive integers!") print('> Recode or partition using "ETC.seq.recode"') return None else: try: out = array("I", seq) if zeroes(out): print("> Input contains 0!") print('> Recode or partition using "ETC.seq.recode" ') return None return out except TypeError as error: print("ERROR:", error) print("> Input must be a list/tuple/array of positive integers!") print('> Recode or partition using "ETC.seq.recode"') return None except OverflowError as error: print("ERROR:", error) print("> Input must be a list/tuple/array of positive integers!") print('> Recode or partition using "ETC.seq.recode"') return None print("No input sequence provided.") return None def recode_lexical(text, case_sensitive=True): if not isinstance(text, str): print("ERROR: Input is not a string.") return None if not case_sensitive: text = text.lower() alphabets = sorted(set(text)) replacer = dict((y, x + 1) for x, y in enumerate(alphabets)) text = cast([replacer[x] for x in text]) return text def recode_alphabetical(text): text = text.lower() if not set(text).issubset(ascii_lowercase): print("> Input contains non alphabetical characters!") return None replacer = dict((y, x + 1) for x, y in enumerate(ascii_lowercase)) text = cast([replacer[x] for x in text]) return text def recode_dna(text): replacer = {"A": 1, "G": 1, "C": 2, "T": 2} text = cast([replacer[x] for x in text.upper()]) return text def recode_random(text): alphabets = list(set(text)) shuffle(alphabets) replacer = dict((y, x + 1) for x, y in enumerate(alphabets)) text = cast([replacer[x] for x in text]) return text def recode_randint(text): alphabets = list(set(text)) numbers = choices(range(1, 2 ** 20), k=len(alphabets)) replacer = dict(zip(alphabets, numbers)) text = cast([replacer[x] for x in text]) return text def partition(seq, n_bins): """ This function takes an input sequence and bins it into discrete points. Parameters ---------- seq : list/tuple of float Collection of floats. n_bins : int Number of bins/paritions to create. Returns ------- list Collection of integers. Contains unique integers from 1 to n_bins. """ assert ( isinstance(n_bins, int) and n_bins > 1 ), "ERROR: Number of bins should be a positive integer" # Get smallest value a = min(seq) # Compute reciprocal of peak-to-peak per bin delta_inv = n_bins / (max(seq) - a + 1e-6) # Transform each element and return return [1 + int((elem - a) * delta_inv) for elem in seq] def partition_numpy(nparr, n_bins): """ This function takes an input sequence & partitions it into equiwidth discrete bins. Min-max scaling, followed by equiwidth binning for each row Parameters ---------- nparr : numpy array, int or float, 2D Each row representing a different sequence. (Columns as time) n_bins : int Number of bins/paritions to create. Returns ------- list Collection of integers. Contains unique integers from 1 to n_bins. """ assert ( isinstance(n_bins, int) and n_bins > 1 ), "ERROR: Number of bins should be a positive integer" assert ( isinstance(nparr, np.ndarray) and nparr.ndim == 2 ), ">ERROR: Input must be 2D NumPy array of numbers" # Get smallest value a = nparr.min(axis=1)[:, np.newaxis] # Compute reciprocal of peak-to-peak per bin delta_inv = n_bins / (nparr.max(axis=1)[:, np.newaxis] - a + 1e-6) # Transform each element and return return 1 + ((nparr - a) * delta_inv).astype("uint32")
# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'responses.ui' # # Created by: PyQt4 UI code generator 4.11.4 # # WARNING! All changes made in this file will be lost! from PyQt4 import QtCore, QtGui try: _fromUtf8 = QtCore.QString.fromUtf8 except AttributeError: def _fromUtf8(s): return s try: _encoding = QtGui.QApplication.UnicodeUTF8 def _translate(context, text, disambig): return QtGui.QApplication.translate(context, text, disambig, _encoding) except AttributeError: def _translate(context, text, disambig): return QtGui.QApplication.translate(context, text, disambig) class Ui_MainWindow(object): def setupUi(self, MainWindow): MainWindow.setObjectName(_fromUtf8("MainWindow")) MainWindow.resize(793, 555) self.centralwidget = QtGui.QWidget(MainWindow) self.centralwidget.setObjectName(_fromUtf8("centralwidget")) self.verticalLayout_2 = QtGui.QVBoxLayout(self.centralwidget) self.verticalLayout_2.setObjectName(_fromUtf8("verticalLayout_2")) self.splitter = QtGui.QSplitter(self.centralwidget) self.splitter.setOrientation(QtCore.Qt.Vertical) self.splitter.setObjectName(_fromUtf8("splitter")) self.tableViewChannels = QtGui.QTableView(self.splitter) sizePolicy = QtGui.QSizePolicy(QtGui.QSizePolicy.Preferred, QtGui.QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.tableViewChannels.sizePolicy().hasHeightForWidth()) self.tableViewChannels.setSizePolicy(sizePolicy) self.tableViewChannels.setFrameShape(QtGui.QFrame.Box) self.tableViewChannels.setAlternatingRowColors(True) self.tableViewChannels.setSelectionMode(QtGui.QAbstractItemView.SingleSelection) self.tableViewChannels.setSortingEnabled(True) self.tableViewChannels.setObjectName(_fromUtf8("tableViewChannels")) self.layoutWidget = QtGui.QWidget(self.splitter) self.layoutWidget.setObjectName(_fromUtf8("layoutWidget")) self.verticalLayout = QtGui.QVBoxLayout(self.layoutWidget) self.verticalLayout.setObjectName(_fromUtf8("verticalLayout")) self.scrollAreaImage = QtGui.QScrollArea(self.layoutWidget) self.scrollAreaImage.setFrameShape(QtGui.QFrame.NoFrame) self.scrollAreaImage.setFrameShadow(QtGui.QFrame.Sunken) self.scrollAreaImage.setWidgetResizable(False) self.scrollAreaImage.setObjectName(_fromUtf8("scrollAreaImage")) self.scrollAreaWidgetContents = QtGui.QWidget() self.scrollAreaWidgetContents.setGeometry(QtCore.QRect(0, 0, 57, 599)) sizePolicy = QtGui.QSizePolicy(QtGui.QSizePolicy.Ignored, QtGui.QSizePolicy.Ignored) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.scrollAreaWidgetContents.sizePolicy().hasHeightForWidth()) self.scrollAreaWidgetContents.setSizePolicy(sizePolicy) self.scrollAreaWidgetContents.setObjectName(_fromUtf8("scrollAreaWidgetContents")) self.scrollAreaImage.setWidget(self.scrollAreaWidgetContents) self.verticalLayout.addWidget(self.scrollAreaImage) self.verticalLayout_2.addWidget(self.splitter) MainWindow.setCentralWidget(self.centralwidget) self.menubar = QtGui.QMenuBar(MainWindow) self.menubar.setGeometry(QtCore.QRect(0, 0, 793, 19)) self.menubar.setObjectName(_fromUtf8("menubar")) self.menuActions = QtGui.QMenu(self.menubar) self.menuActions.setObjectName(_fromUtf8("menuActions")) self.menu_Options = QtGui.QMenu(self.menubar) self.menu_Options.setObjectName(_fromUtf8("menu_Options")) MainWindow.setMenuBar(self.menubar) self.statusbar = QtGui.QStatusBar(MainWindow) self.statusbar.setObjectName(_fromUtf8("statusbar")) MainWindow.setStatusBar(self.statusbar) self.actionInitialize_from_current_folder = QtGui.QAction(MainWindow) self.actionInitialize_from_current_folder.setObjectName(_fromUtf8("actionInitialize_from_current_folder")) self.actionSet_to_artifact = QtGui.QAction(MainWindow) self.actionSet_to_artifact.setObjectName(_fromUtf8("actionSet_to_artifact")) self.actionSet_to_maybe = QtGui.QAction(MainWindow) self.actionSet_to_maybe.setObjectName(_fromUtf8("actionSet_to_maybe")) self.actionSet_to_edit = QtGui.QAction(MainWindow) self.actionSet_to_edit.setObjectName(_fromUtf8("actionSet_to_edit")) self.actionSet_to_response = QtGui.QAction(MainWindow) self.actionSet_to_response.setObjectName(_fromUtf8("actionSet_to_response")) self.action_Fit_image_size = QtGui.QAction(MainWindow) self.action_Fit_image_size.setCheckable(True) self.action_Fit_image_size.setObjectName(_fromUtf8("action_Fit_image_size")) self.action_Save_classification_to_file = QtGui.QAction(MainWindow) self.action_Save_classification_to_file.setObjectName(_fromUtf8("action_Save_classification_to_file")) self.actionSet_to_no_response = QtGui.QAction(MainWindow) self.actionSet_to_no_response.setObjectName(_fromUtf8("actionSet_to_no_response")) self.actionSet_to_okay = QtGui.QAction(MainWindow) self.actionSet_to_okay.setObjectName(_fromUtf8("actionSet_to_okay")) self.action_goto_next = QtGui.QAction(MainWindow) self.action_goto_next.setObjectName(_fromUtf8("action_goto_next")) self.menuActions.addAction(self.actionInitialize_from_current_folder) self.menuActions.addSeparator() self.menuActions.addAction(self.actionSet_to_okay) self.menuActions.addAction(self.actionSet_to_edit) self.menuActions.addAction(self.actionSet_to_artifact) self.menuActions.addSeparator() self.menuActions.addAction(self.actionSet_to_response) self.menuActions.addAction(self.actionSet_to_maybe) self.menuActions.addAction(self.actionSet_to_no_response) self.menuActions.addSeparator() self.menuActions.addAction(self.action_Save_classification_to_file) self.menuActions.addAction(self.action_goto_next) self.menu_Options.addAction(self.action_Fit_image_size) self.menubar.addAction(self.menuActions.menuAction()) self.menubar.addAction(self.menu_Options.menuAction()) self.retranslateUi(MainWindow) QtCore.QMetaObject.connectSlotsByName(MainWindow) def retranslateUi(self, MainWindow): MainWindow.setWindowTitle(_translate("MainWindow", "MainWindow", None)) self.menuActions.setTitle(_translate("MainWindow", "&Actions", None)) self.menu_Options.setTitle(_translate("MainWindow", "&Options", None)) self.actionInitialize_from_current_folder.setText(_translate("MainWindow", "&Initialize from current folder", None)) self.actionInitialize_from_current_folder.setShortcut(_translate("MainWindow", "Ctrl+I", None)) self.actionSet_to_artifact.setText(_translate("MainWindow", "Set to \'&artifact\'", None)) self.actionSet_to_artifact.setShortcut(_translate("MainWindow", "A", None)) self.actionSet_to_maybe.setText(_translate("MainWindow", "Set to \'&maybe response\'", None)) self.actionSet_to_maybe.setShortcut(_translate("MainWindow", "M", None)) self.actionSet_to_edit.setText(_translate("MainWindow", "Set to \'needs &edit\'", None)) self.actionSet_to_edit.setShortcut(_translate("MainWindow", "E", None)) self.actionSet_to_response.setText(_translate("MainWindow", "Set to \'&response\'", None)) self.actionSet_to_response.setShortcut(_translate("MainWindow", "R", None)) self.action_Fit_image_size.setText(_translate("MainWindow", "&Fit image size", None)) self.action_Save_classification_to_file.setText(_translate("MainWindow", "&Save classification to file", None)) self.action_Save_classification_to_file.setShortcut(_translate("MainWindow", "Ctrl+S", None)) self.actionSet_to_no_response.setText(_translate("MainWindow", "Set to \'&no response\'", None)) self.actionSet_to_no_response.setShortcut(_translate("MainWindow", "N", None)) self.actionSet_to_okay.setText(_translate("MainWindow", "Set to &okay", None)) self.actionSet_to_okay.setShortcut(_translate("MainWindow", "O", None)) self.action_goto_next.setText(_translate("MainWindow", "Move to next row", None)) self.action_goto_next.setShortcut(_translate("MainWindow", "Space", None))
""" methods related to conditional likelihood of the data for class ChangepointModel """ import numpy as np import scipy.special as special def L_(self, i, seg): x_ = self.x[i][seg[0]:seg[1]] out = 0.0 if self.x_distr[i] == 'Poisson': # hyper[0] is alpha, hyper[1] is gamma n = len(x_) x_dot = np.sum(x_) out = self.x_hyper[i][0] * np.log(self.x_hyper[i][1]) - special.gammaln(self.x_hyper[i][0]) out += special.gammaln(x_dot + self.x_hyper[i][0]) - (x_dot + self.x_hyper[i][0]) * np.log(self.x_hyper[i][1] + n) elif self.x_distr[i] == 'Normal': # hyper[0] is lambda (scale), hyper[1] is mu_0 and hyper[2] is lambda_0 (scale) n = len(x_) x_dot = np.sum(x_) x_dot_sq = np.sum(np.square(x_)) lda_n = self.x_hyper[i][2] + n * self.x_hyper[i][0] mu_n = (self.x_hyper[i][1] * self.x_hyper[i][2] + self.x_hyper[i][0] * x_dot) / lda_n out = 0.5 * np.log(self.x_hyper[i][2] / lda_n) + (n / 2.) * np.log(self.x_hyper[i][0] / (2 * np.pi)) out += lda_n * (mu_n ** 2) - self.x_hyper[i][0] * x_dot_sq - self.x_hyper[i][2] * (self.x_hyper[i][1] ** 2) elif self.x_distr[i] == 'Multinomial': # hyper[0] are concentration parameters, hyper[1] (used for simulations only) alpha_sum = sum(self.x_hyper[i][0]) x_sums = x_.sum(0) out = special.gammaln(alpha_sum) - np.sum(special.gammaln(self.x_hyper[i][0])) out += np.sum(special.gammaln(x_sums+np.array(self.x_hyper[i][0]))) - special.gammaln(sum(x_sums)+alpha_sum) return out def llikelihood_ratio_birth(self, i, j): tau_beg = self.ltau[i][j-1] + self.d[i][j-1] - 1 tau_p = self.ltau[i][j] + self.d[i][j] - 1 tau_end = self.ltau[i][j+1] + self.d[i][j+1] - 1 out = self.L_(i, [tau_beg, tau_p]) + self.L_(i, [tau_p, tau_end]) - self.L_(i, [tau_beg, tau_end]) return out def llikelihood_adjacent_segments(self, i, j): tau_beg = self.ltau[i][j-1] + self.d[i][j-1] - 1 tau_j = self.ltau[i][j] + self.d[i][j] - 1 tau_end = self.ltau[i][j+1] + self.d[i][j+1] - 1 out = self.L_(i, [tau_beg, tau_j]) + self.L_(i, [tau_j, tau_end]) return out def cond_llikelihood_x(self, i): out = 0.0 for j in range(len(self.ltau[i])): tau_beg = self.ltau[i][j-1] + self.d[i][j-1] - 1 tau_end = self.ltau[i][j] + self.d[i][j] - 1 out += self.L_(i, [tau_beg, tau_end]) return out